Dr. Evangelos Vlachos

Channel Estimation for mmWave and THz Massive MIMO

Mon, 19 Oct 2026 00:00:00 +0000

Channel estimation is how a receiver learns the paths a radio signal has travelled: their directions, delays and strengths. At millimeter-wave (mmWave) and terahertz (THz) frequencies, where 5G and 6G networks find wide bandwidth, signals fade quickly and large antenna arrays must focus their energy into narrow beams. A beam can only be pointed at a channel that has been estimated well, so channel estimation decides how much of the promised bandwidth is actually usable. We have worked on this problem since 2018: for massive MIMO base stations, low-cost receivers, moving drones, extremely large THz arrays and, more recently, quantum computers.

Why channel estimation is hard at mmWave and THz

Cheap hardware hides the signal. Large arrays are affordable only with hybrid analog/digital beamforming, few radio-frequency (RF) chains and low-resolution analog-to-digital converters (ADCs), so the receiver sees a compressed and coarsely quantized version of the channel.
There is little time. The channel changes quickly, especially for vehicles and UAVs, so only short training sequences can be afforded.
Wide bandwidth bends the beam. Over a very wide band the beam direction drifts with frequency (beam squint) and echoes spread over many symbols. At THz both effects appear together, which is called dual-wideband fading.

Sparse and low-rank channel estimation from short training

mmWave channels have only a few strong paths, so the channel matrix is sparse in the beamspace and has low rank. We exploit both properties at once, through matrix completion and the alternating direction method of multipliers (ADMM), to recover the channel from far shorter training than either property alone allows (IEEE SPL 2018). For wideband hybrid receivers we introduced a random spatial sampling stage in the analog combiner, so that a few RF chains capture enough of the channel to recover it (IEEE JSTSP 2019, SPAWC 2019), and we extended the method to track the channel as it changes over time (ICASSP 2020).

Hybrid beamforming receivers with low-resolution ADCs

Few-bit ADCs turn the received signal into a staircase. We add small random signals (dither) to the analog beamformer and to the ADCs, so that quantization becomes informative rather than destructive, and recover the channel with an expectation-maximization estimator that performs close to an oracle least-squares estimator (ICASSP 2018). For few-bit receivers we also developed a message-passing estimator that needs no prior knowledge of the channel statistics (EUSIPCO 2018). With colleagues at Queen’s University Belfast we studied a two-stage Rotman lens beamformer, which gives high antenna gain without a beam-selection network, and designed a robust estimator for it under low-resolution sampling (SPAWC 2019, ICC 2023 Workshops).

Channels of moving UAVs and vehicles

A drone or a car already knows where it is and how fast it moves. We feed this information from the control system into the channel estimator. For a UAV link we first estimate the UAV position from the line-of-sight path and then complete the rest of the channel (Asilomar 2020). The vehicle’s velocity reduces the training needed under beam squint (EUSIPCO 2023), and for a UAV with a single RF chain the state estimate from its flight controller makes the wideband channel recoverable from short training (IEEE TVT 2025).

Test flights around a base station (top) and the error of the UAV’s own position estimate (bottom), the side information our estimator uses. From IEEE TVT 2025.

THz XL-MIMO: dual-wideband fading and sensing-aided estimation

At THz, extremely large arrays (XL-MIMO) with hundreds of antennas face beam squint and inter-symbol interference at the same time. We model both in the time domain for single-carrier transmission, with a mixed-integer biconvex formulation that handles arbitrary delay patterns (EUSIPCO 2025). In an integrated sensing and communication (ISAC) system the base station’s own radar output gives the estimator a head start: a biconvex ADMM algorithm warm-started by the radar jointly recovers the channel matrix and the delay support, and returns the delay profile as a sensing output at no extra pilot cost (ISAC 2026).

Channel estimation with reconfigurable intelligent surfaces

A reconfigurable intelligent surface (RIS) can shape the propagation environment, but the channels through it must be estimated first, and a fully passive surface cannot observe them. We proposed an RIS hardware architecture with a minimal number of active elements that makes explicit channel estimation possible (ICASSP 2020), and a protocol for receiving RISs that estimate the channels of two multi-antenna terminals from non-orthogonal pilots, again exploiting beamspace sparsity and low rank (Springer book chapter, 2026).

Quantum computing for channel estimation

Many channel estimators reduce to solving a large system of linear equations, a task where quantum algorithms promise large speed-ups. With Kostas Blekos we studied how quantum linear-system solvers could assist mmWave massive MIMO receivers, and how the amplitude encoding of the quantum state relates to recovering the channel (VLSI-SoC 2022).

Current directions

Closing the loop between sensing and communication, so that each channel estimate improves the next sensing step and the sensing output improves the next estimate.
Near-field (spherical-wave) propagation for XL-MIMO arrays, where the far-field model behind most estimators stops holding.
Joint design of UAV motion and beam alignment, so that the flight path itself keeps the channel easy to estimate.

Collaborators

George C. Alexandropoulos, John S. Thompson, Aryan Kaushik, Christos Mavrokefalidis, Kostas Berberidis, Michail Matthaiou, Vincent F. Fusco, Muhammad Z. Shakir, Besma Smida, Konstantinos D. Katsanos and Kostas Blekos.

Work with us

We offer diploma and MSc theses, internships and PhD co-supervision on these problems, at ISI, ATHENA RC and at the University of Patras. Students get a well-defined problem, working simulation code to start from and compute time on the ARIS national supercomputer, and they co-author what they publish. Open thesis and internship topics are listed on the open topics page. Researchers interested in joint papers or proposals are equally welcome. Get in touch, and see the research group page for who works with us today.

Open Topics for Theses, Internships and PhDs

Mon, 19 Oct 2026 00:00:00 +0000

Our group works on the joint design of sensing, communication and control for UAV networks and 6G. The topics below are open now. Each starts from a published paper of ours, comes with working code to begin from, and is scoped to end in a result that can be published with you as a co-author. Diploma and MSc theses are offered with the University of Patras, PhD topics through co-supervision, and internships at ISI, ATHENA RC. Simulations run on the ARIS national supercomputer. Hardware topics use the drones, mmWave radar and Wi-Fi/Remote ID equipment of the EUSOME project.

Sensing and estimation

1. Sensing-aided channel estimation for UAV mmWave links

MSc or diploma thesis · simulation in Python or MATLAB

A drone’s flight controller already knows its position and velocity, and a base station with radar can sense the drone itself. Use both to cut the training a wideband mmWave link needs, starting from our estimator in IEEE TVT 2025 and the sensing-aided method of ISAC 2026. Deliverable: a simulator with beam squint and a comparison of pilot overhead against standard estimators.

2. Cooperative localization for UAV swarms over lossy links

MSc thesis or PhD · estimation theory and simulation

Drones in a swarm can locate each other from mutual range and bearing measurements when GPS is degraded, but links drop and some measurements are wrong. Starting from our consistency-gated method in EUSIPCO 2026, design estimators that stay consistent under packet loss and outliers, and test them on a simulated swarm in Gazebo/PX4.

Communication and control

3. Connectivity-aware motion planning for UAV formations with directional links

Diploma or MSc thesis · ROS 2, Gazebo/PX4

mmWave links work only when the beams of both drones point at each other, so motion and communication cannot be planned separately. Building on our predictive control for connectivity in NextGCom 2026, implement a planner that keeps a formation connected while it flies its mission, with a directional link model in Gazebo/PX4.

4. Safe collision avoidance and interception with control barrier functions

MSc thesis or PhD · control theory and simulation, optionally hardware

Our SE(3) control barrier functions in ICUAS 2026 keep a counter-UAS interceptor safe under its hardware limits. Extend them to actuator saturation and sensing delays, to several interceptors, and to hardware-in-the-loop tests on small quadrotors.

Drone detection and air mobility

5. Passive Wi-Fi radar and Remote ID for drone detection

Diploma or MSc thesis, or internship · ESP32 and Wi-Fi hardware, signal processing

Ordinary Wi-Fi signals reflected by a drone reveal it, and Remote ID broadcasts identify it. Build a low-cost detector that combines both and evaluate it with measurements from the EUSOME field trials. Our radar simulation framework in ISC2 2025 is the starting point for the radar side.

6. Radar and camera fusion for detect-and-avoid on small drones

Diploma or MSc thesis · Python, deep learning, mmWave radar

A camera fails in low light and a radar gives few pixels; together they can detect obstacles and other aircraft reliably. Train and test fusion detectors on the synchronized radar and camera data of FIRA-DAV, for the detect-and-avoid function of SPLASH.

7. Cellular ISAC for drone surveillance in U-space

MSc thesis or PhD · simulation and standards

5G and 6G base stations can act as radars and track drones. Quantify the accuracy and coverage they reach, and what this means for the separation between drones in U-space, starting from our study of ISAC for advanced air mobility in ISC2 2025. The results relate to our contribution to the ETSI ISG ISAC standardisation group.

What you get

A well-defined problem, code to start from, a weekly meeting and the Friday group seminar, compute time on ARIS, and co-authorship of what you publish.

How to apply

Write to us with the topic you are interested in, a CV or transcript, and something you have built: code, a report, a project. Your own ideas within these directions are welcome too.

Research Group

Fri, 16 Oct 2026 00:00:00 +0000

Leading a Special Session at IEEE ICASSP 2027

Wed, 14 Oct 2026 00:00:00 +0000

I am leading the organisation of a special session at IEEE ICASSP 2027, the flagship conference of the IEEE Signal Processing Society, which takes place in Toronto, Canada, on 16–21 May 2027.

Special session: Signal Processing for Integrated Sensing, Communication, and Control in Autonomous Aerial Networks

Drones and other autonomous aerial platforms have to sense their surroundings, keep reliable radio links and stay under safe control, often with the same hardware and under tight power and latency limits. The session brings together signal processing work that treats these three functions jointly rather than one at a time.

The session is co-organised with Kostas Berberidis (University of Patras), George C. Alexandropoulos (National and Kapodistrian University of Athens) and Panayiotis Kolios (KIOS Research and Innovation Center of Excellence, University of Cyprus).

More information: ICASSP 2027

NextGCom 2026 in Kefalonia: Session Chair and Paper Presentation

Tue, 29 Sep 2026 00:00:00 +0000

I attended the 2026 Next Generation Communications conference (NextGCom 2026), held on 28-30 September 2026 at the Ionian University in Argostoli, Kefalonia, Greece.

On 29 September I chaired Special Session 3: Integrated Sensing and AI-Driven Communication in 6G Perceptive Mobile Networks, which brought together four papers on sensing-aided and AI-driven physical-layer design for 6G. In the same session I presented our paper:

Semantically-Informed Predictive Control for Autonomous UAV Connectivity in Urban Environments

Evangelos Vlachos, Dimitrios Tsourounis, and Aryan Kaushik

The work fuses radar sensing with visual semantic classification into an effective capacity metric, so that a UAV path planner avoids both hard blockers and unreliable multipath zones in mmWave urban links.

Title slide, NextGCom 2026

Sensing-assisted communication for UAV links

Our group also presented Geometry-Aware Regularization for Deep-Unfolded MIMO-OTFS Detection (Nikolaos Ntavanelos and Evangelos Vlachos) in the technical sessions.

This work is part of the EUSOME project, funded by the European Union under Horizon Europe Grant Agreement 101187121.

Privacy Policy

Thu, 24 Sep 2026 00:00:00 +0000

This website is the personal academic page of Evangelos Vlachos. The site itself does not ask you for personal data. It uses Google Analytics to count visits.

Personal tools. I also register a few small tools with Google, such as the OAuth client “rclone-compy” that backs up my own computers to my own Google Drive. These tools are used only by me and are not offered to anyone else:

They access only my own Google account, after I sign in and approve access.
Data they read or write stays on my own devices and in my own Google Drive.
Nothing is sold, shared with third parties, or used for advertising.
Access can be revoked at any time from the Google account’s security settings.

Questions: please use the contact page.

Terms of Service

Thu, 24 Sep 2026 00:00:00 +0000

This website and the small personal tools linked to it (for example the Google OAuth client “rclone-compy”) are provided by Evangelos Vlachos for personal, non-commercial use.

The tools are not a public service; they are used only by their owner.
Everything is provided as is, without warranty of any kind.
Content on this site may be cited with attribution; publications remain under the copyright of their publishers where applicable.
Teaching page photos are from Wikimedia Commons: University of Patras by Victoria Daskalou (CC BY-SA 3.0), University of Edinburgh by Chrysi Chrysochou (CC BY-SA 3.0), University of the Peloponnese by Kikisoti (CC BY-SA 4.0).

See also the privacy policy. Questions: please use the contact page.

Awarded a University of Jyväskylä Visiting Fellowship for 2027

Wed, 16 Sep 2026 00:00:00 +0000

I am happy to share that I have been awarded a Visiting Fellowship by the University of Jyväskylä (JYU), Finland, for a one-month research visit during 2027.

The visit will be hosted by Prof. Markus Miettinen, Professor of Cybersecurity at JYU. We will work on cybersecurity for UAV systems, starting with the trustworthiness of drone Remote ID, combining my work on UAV sensing and control with his group’s expertise in security for AI and networked systems.

Paper Accepted at IEEE ISAC 2026

Sat, 12 Sep 2026 00:00:00 +0000

We are pleased to announce that our paper has been accepted at the 2026 IEEE 1st Annual Integrated Sensing and Communication Conference (ISAC 2026):

Biconvex ADMM for Sensing-Aided XL-MIMO Channel Estimation at the THz Frequency Band

Evangelos Vlachos and George C. Alexandropoulos

Terahertz XL-MIMO links suffer from dual-wideband fading, where beam squint and multipath delay spread couple angle and delay estimation across hundreds of antenna pairs. The paper uses the base station’s monostatic radar output to warm-start an ADMM estimator that jointly recovers the channel and its binary delay support, at no extra pilot cost, and closely approaches the Cramér-Rao Bound.

New Paper: Consistency-Gated Cooperative Localization for UAV Swarms under Intermittent GPS

Fri, 11 Sep 2026 00:00:00 +0000

New paper indexed on Google Scholar: “Consistency-Gated Cooperative Localization for UAV Swarms under Intermittent GPS” (2026).

Publication page

New Paper: Hardware-Aware Control Barrier Functions for Counter-UAS Interceptors with Directed Energy Payloads

Fri, 11 Sep 2026 00:00:00 +0000

New paper indexed on Google Scholar: “Hardware-Aware Control Barrier Functions for Counter-UAS Interceptors with Directed Energy Payloads” (2026).

Publication page · DOI

New Paper: Intelligent UAV Path Planning for Ergodic Rate Maximization of MIMO Multipath Channels

Fri, 11 Sep 2026 00:00:00 +0000

New paper indexed on Google Scholar: “Intelligent UAV Path Planning for Ergodic Rate Maximization of MIMO Multipath Channels” (2025).

Publication page

Reached 1300+ Citations on Google Scholar

Fri, 11 Sep 2026 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 1300 times according to Google Scholar (current count: 1318).

Geometry-Aware Regularization for Deep-Unfolded MIMO-OTFS Detection

Mon, 24 Aug 2026 00:00:00 +0000

Key Contributions

Gram matrix analysis: Analytical characterization of the non-uniform diagonal structure of the MIMO-OTFS Gram matrix, proving that all diagonal variation arises exclusively from inter-path coupling.
Geometry-aware regularizer: A coordinate-wise regularizer embedded into a deep-unfolded Conjugate Gradient detector, with per-component weights learned in a data-driven manner.
Performance gains: The proposed detector outperforms low-complexity baselines and approaches heavier neural architectures at medium-to-high SNR, with improved robustness in high-mobility scenarios.

Semantically-Informed Predictive Control for Autonomous UAV Connectivity in Urban Environments

Mon, 24 Aug 2026 00:00:00 +0000

Key Contributions

An Effective Capacity metric that extends the radio map paradigm by weighting raw throughput with a material-dependent reliability coefficient, decoupling signal strength from link stability and exposing permeable vegetation corridors that signal-strength planners overlook.
A two-layer planner comprising (i) a Semantically-Informed RRT backbone augmented with material-dependent reliability and capacity acceptance checks that prune expansion through multipath zones, and (ii) a gradient-based trajectory optimizer that deforms the path by balancing signal-seeking and reliability-seeking forces derived from the product-rule decomposition of ∇C_eff.
Hardware validation of the material ambiguity premise via characterization with a TI IWR6843AOP 60 GHz FMCW radar, confirming that radar alone cannot distinguish concrete from vegetation.

Hardware-Aware SE(3) Control Barrier Functions for Counter-UAS Interceptors with Directed Energy Payloads

Wed, 17 Jun 2026 00:00:00 +0000

Simulation Demo

The animation below compares two methods on the SE(3) CBF scenario: the unfiltered controller (red) and the proposed ADMM-CBF safety filter (blue), which maintains FoV pointing throughout the pursuit maneuver.

Key Contributions

A hardware-aware CBF safety filter on the SE(3) manifold that provides formal FoV pointing guarantees for directed-energy payloads under aggressive pursuit dynamics.
Formulation of phased-array electronic steering limits as a relative-degree-two safety constraint with analytically verified Lie derivatives.
An ADMM solver with constant-time core factorization enabling real-time onboard execution.
Validation in both a nonlinear SE(3) simulation and a PX4 SITL environment, demonstrating elimination of all FoV violations and recovery of antenna gain through beam tightening.

Biconvex ADMM for Sensing-Aided XL-MIMO Channel Estimation at the THz Frequency Band

Sat, 13 Jun 2026 00:00:00 +0000

Key Contributions

Biconvex ADMM with binary support enforcement: We show that the THz XL-MIMO dual-wideband channel concentrates onto a shared binary delay support of drastically reduced dimension, and develop a biconvex ADMM where iterative hard thresholding (IHT) exactly enforces this structure — outperforming continuous-valued sparse recovery and oracle-LS baselines in the barely-determined regime.
Sensing-aided warm start at no pilot cost: The base station’s monostatic radar output initialises the delay-support estimate, yielding provably exact support recovery beyond a pilot-length threshold, with no additional pilot overhead.
CRB proximity: The proposed estimator closely approaches the Cramér-Rao Bound and closes the ISAC sensing loop by delivering the recovered delay profile as a radar output.

Consistency-Gated Cooperative Localization for UAV Swarms Under Intermittent GPS

Tue, 02 Jun 2026 00:00:00 +0000

Key Contributions

Consistency-gated framework: A cooperative localization scheme that actively prevents harmful cooperative updates when inter-agent measurements are inconsistent under intermittent GPS.
Two-stage estimation: Decoupled architecture separating local observability recovery (EKF with GPS/IMU) from cooperative refinement (distributed ADMM over UWB range and AoA constraints).
Lightweight acceptance gate: A learning-based gate driven solely by runtime residual and consensus indicators — no ground truth required online.
Quantitative gains: Demonstrated moderate improvements in absolute accuracy and substantial gains in formation-relevant relative accuracy over GPS/IMU baselines.

Paper Submitted to IEEE ISAC 2026

Mon, 01 Jun 2026 00:00:00 +0000

We submitted a new paper to the 2026 IEEE International Symposium on Joint Communications and Sensing (ISAC 2026):

Sensing-Aided THz XL-MIMO Channel Estimation via Biconvex ADMM

Evangelos Vlachos and George C. Alexandropoulos

Terahertz XL-MIMO systems face a dual-wideband fading challenge: ultra-wide bandwidth introduces simultaneous spatial beam squint and temporal multipath spread, coupling angle and delay estimation across hundreds of antenna pairs. This work proposes a sensing-aided framework that exploits the base station’s monostatic radar output to warm-start a biconvex ADMM algorithm, jointly recovering the channel matrix and the binary delay-support vector at no additional pilot overhead.

Key contributions:

Binary delay-support structure: We show that the THz XL-MIMO dual-wideband channel concentrates onto a shared binary delay support of drastically reduced dimension, and develop a biconvex ADMM where iterative hard thresholding (IHT) exactly enforces this structure.
Sensing-aided warm start: The monostatic radar output initialises the delay-support estimate, yielding provably exact support recovery beyond a pilot-length threshold — with no extra pilot cost and simultaneously delivering the delay profile as a sensing output.
CRB proximity: The proposed estimator closely approaches the Cramér-Rao Bound, strictly outperforming sparse recovery and oracle-LS baselines in the barely-determined regime.

Paper Accepted at EUSIPCO 2026

Mon, 11 May 2026 00:00:00 +0000

We are pleased to announce that our paper has been accepted at the 34th European Signal Processing Conference (EUSIPCO 2026), to be held in Bruges, Belgium:

Consistency-Gated Cooperative Localization for UAV Swarms Under Intermittent GPS

Dimitrios Venetis, Evangelos Vlachos, and Konstantinos Berberidis

This paper addresses the problem of cooperative localization for UAV swarms when GPS is intermittently available. Each agent maintains a local state estimate via an Extended Kalman Filter (EKF), and inter-agent cooperation is achieved through a MAP refinement stage using UWB range and Angle-of-Arrival constraints, solved via ADMM. A consistency gate driven by ADMM residuals and normalized measurement errors selectively accepts cooperative measurements, preventing degradation from inconsistent observations.

The conference proceedings will be published in the open-access EURASIP archive and on IEEE Xplore.

Paper Accepted at ICUAS 2026

Tue, 21 Apr 2026 00:00:00 +0000

We are pleased to announce that our paper has been accepted at the 2026 International Conference on Unmanned Aircraft Systems (ICUAS):

Hardware-Aware SE(3) Control Barrier Functions for Counter-UAS Interceptors with Directed Energy Payloads

Evangelos Vlachos, Panayiotis Kolios, and Christos Skliros

This paper addresses the safety-critical control problem of keeping a directed-energy phased-array payload’s boresight aimed at a target UAS during high-speed interception. We develop SE(3) Control Barrier Functions that enforce a field-of-view constraint on the interceptor’s attitude while respecting the physical limits of the onboard hardware, solved efficiently via ADMM at high update rates.

This work is supported by the EU Horizon Europe project EUSOME (Grant Agreement No. 101187121).

Submitted: AERIAL-EDGE — Twinning for Aerial Radio Measurement and Edge Data Intelligence

Thu, 09 Apr 2026 00:00:00 +0000

We submitted AERIAL-EDGE to the Horizon Europe Twinning call (HORIZON-WIDERA-2026-02) today, with ATHENA RC as coordinator.

Full title: AERIAL-EDGE — Twinning for Aerial Radio Measurement and Edge Data Intelligence for Mediterranean Drone Corridors

AERIAL-EDGE addresses a critical gap for U-Space deployment: the lack of calibrated, open aerial channel datasets and the edge data infrastructure to turn raw radio measurements into actionable drone corridor intelligence. The project will establish a permanent AERIAL Lab at ATHENA RC through structured knowledge transfer from advanced European partners.

Consortium (€1,500,000 total, 36 months):

ATHENA RC, Greece (Coordinator)
TU Ilmenau, Germany
German Aerospace Center (DLR), Germany
University of Sussex, UK
KIOS Research and Innovation Centre, University of Cyprus

This work builds on ATHENA RC’s aerial communications experience in the EUSOME project.

Receiving RISs: Enabling Channel Estimation and Autonomous Configuration

Wed, 01 Apr 2026 00:00:00 +0000

Key Contributions

Proposes a semi-passive RIS architecture with RF chains that enables active channel estimation via tunable absorption phase profiles, eliminating the need for external pilot injection.
Introduces a channel estimation protocol exploiting beamspace sparsity and low-rank structure of extremely large MIMO channels, solved efficiently via ADMM.
Demonstrates how RIS-side channel estimates can be leveraged to autonomously optimize phase-quantized reflection coefficients, enabling self-configuring RIS operation.

High-Performance Computing Enables Digital Twins for UAV Operations

Wed, 25 Mar 2026 00:00:00 +0000

Did you know that integrating drones safely into urban airspace is not only a regulatory challenge, but also a computational one?

To build realistic Digital Twins of UAV operations in complex urban environments, we need to simultaneously model several systems:

Terahertz & mmWave propagation across dense 3D city geometries using ultra-massive MIMO systems
Multimodal AI sensor fusion (Vision + Radar) that remains reliable even in low-visibility conditions
Large-scale trajectory optimization that guarantees safe and energy-efficient UAV flight

Each of these challenges quickly explodes in complexity — reaching tens of millions of unknowns, massive GPU memory requirements, and millions of optimization constraints. This is where High-Performance Computing (HPC) becomes the enabler in the EUSOME Project.

With access to ARIS HPC systems and NVIDIA A100 GPU nodes, researchers can move beyond simplified models and start building high-fidelity Digital Twins capable of supporting real-world autonomous UAV operations. The EUSOME project aims to bridge this gap between theoretical research and deployable airspace systems, supporting the future of U-space and autonomous aviation in Europe.

Exploring 5G Connectivity for UAV Operations

Wed, 18 Mar 2026 00:00:00 +0000

Within the EUSOME Project, partners from the Industrial Systems Institute / Research Centre ATHENA and Hellenic Drones S.A. recently carried out drone flights to measure 5G signal strength at different altitudes in urban environments.

The objective was to better understand how buildings and other terrestrial obstacles affect connectivity in the airspace where drones typically operate (around 20–60 meters). The measurements collected during these flights will help improve how UAV missions are planned, supporting safer and more reliable drone operations for applications such as inspections, environmental monitoring, and emergency response.

Advancing connectivity awareness is an important step toward enabling scalable drone operations in the smart cities of the future.

Throwback to the 2nd EUSOME study visit in Belgrade

Tue, 03 Mar 2026 00:00:00 +0000

Throwback to the 2nd EUSOME Project study visit in Belgrade. On 25-26 November, project partner Institute Mihajlo Pupin in Belgrade hosted the second study visit of the EUSOME Mentoring & Training Programme. The visit brought together partners from project coordinator KIOS Research and Innovation Center of Excellence of the University of Cyprus and project partner the Centrum Badań Kosmicznych Polskiej Akademii Nauk, along with Serbian stakeholders, to exchange views on the future of Advanced Air Mobility (AAM) in the region Constructive discussions with representatives of the Civil Aviation Directorate of the Republic of Serbia, the Emergency Management Sector of Serbia and Post of Serbia focused on the current UAS regulatory framework in Serbia and the practical requirements for enabling AAM operations. The programme also included a visit to IMP-Computer Systems Ltd., where participants were introduced to advanced simulation and unmanned aircraft control systems, highlighting the technological capabilities supporting future applications Through this kind of mentoring activities and stakeholder dialogue, the EUSOME programme continues to strengthen regional cooperation and align innovation with real operational needs EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

EUSOME Design: Light for its volume - adaptable for every mission

Fri, 27 Feb 2026 00:00:00 +0000

EUSOME `` Design: Light for its volume - adaptable for every mission! As part of the EUSOME Project, the eVTOL aircraft currently in the design phase, led by project partner Aviomania Aircraft, is being engineered to: Achieve under 100 kg empty weight while offering significant internal volume. Provide a comfortable, modular interior for one person. Support a wide range of missions, from piloted to remote operations. By combining lightweight construction with generous space, the aircraft is designed to adapt to different tasks and payload requirements without compromising efficiency or safety.EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Our 1st Science Café

Thu, 12 Feb 2026 00:00:00 +0000

Our 1st Science Café!The Science Café on Advanced Air Mobility (AAM) took place at the University of Cyprus on the 28th of January and brought together students, researchers, and professionals to explore how drones can serve society and the public good Through open discussions, live polls, and hands-on exercises, the focus went beyond the tech. Participants talked about responsible use, real community needs, and how people can play an active role in shaping the future of AAM The event was organised by SocialTech Lab and the KIOS Research and Innovation Center of Excellence, as part of the public outreach activities of the EUSOME Project. More open, participatory AAM dialogues are coming! Stay tuned EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

3rd EUSOME Study Visit — Belgrade, Serbia

Tue, 03 Feb 2026 00:00:00 +0000

I travelled to Belgrade, Serbia to participate in the 3rd EUSOME Study Visit, hosted by our consortium partner the Institute Mihajlo Pupin (IMP).

The study visit brought together researchers and stakeholders from across the EUSOME consortium for a day of technical presentations, knowledge exchange, and bilateral collaboration meetings, covering advances in U-Space technologies, unmanned aerial systems, and integrated sensing and communications.

The event was organized by Dr. Valentina Janev and colleagues at IMP, and took place on February 3, 2026.

This visit is part of the ongoing dissemination and knowledge-transfer activities of the EUSOME project (EU Horizon Europe, Grant Agreement No. 101187121), which aims to establish an Excellence Hub bridging European 6G research and aviation regulation in the U-Space domain.

A highlight from 2025: EUSOME at Oshkosh

Tue, 03 Feb 2026 00:00:00 +0000

A highlight from 2025: EUSOME at Oshkosh! Back in July 2025, the Aviomania Aircraft team had the chance to showcase the EUSOME Project vision at EAA AirVenture Oshkosh 2025 in Wisconsin, USA. At their dedicated booth, our partners presented a 3D-printed model of their eVTOL concept and shared insights into the EUSOME project’s goals and key technological innovations. It was a fantastic opportunity to connect with aviation enthusiasts, exchange ideas, and discuss the future of urban and personal air mobility. The conversations and feedback received at Oshkosh continue to shape our work today, supporting the ongoing development of flexible, lightweight, mission-ready eVTOL designs. EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Join our Science Café on Advanced Air Mobility (AAM)

Tue, 03 Feb 2026 00:00:00 +0000

Join our Science Café on Advanced Air Mobility (AAM). On 28 January 2026, join EUSOME project partners SocialTech Lab and KIOS Research and Innovation Center of Excellence at the University of Cyprus for an interactive Science Café on drones and Advanced Air Mobility. This is an open space where everyone can learn, discuss, and share their perspective on how drones can support healthcare, emergency response, sustainability, and more. UCY Engineering Faculty, POL 10 - Amphitheatre B102 “Elias Kyriakides” 16:00 - 18:00 Register by 25 January: https://lnkd.in/gWNbGinp

EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Workshop Proposal Submitted to EuCNC & 6G Summit 2026

Thu, 22 Jan 2026 00:00:00 +0000

As part of the WP7 Dissemination activities of the EUSOME project, we submitted a proposal to host a dedicated Half-Day Workshop at the EuCNC & 6G Summit 2026 (Malaga, Spain, June 2–5, 2026).

Workshop Title: Integrated Sensing and Communications for the 6G Sky: Bridging Technology and Regulation in U-Space

The workshop is co-organized with partners from the University of Cyprus (KIOS Research and Innovation Centre of Excellence) and will feature contributions from DCAC and HCAA on the regulatory side.

Programme structure:

Session 1 — Technical: Presentations on 6G non-terrestrial communications, ISAC millimeter-wave communications, and joint control and communications. Led by ATHENA RC and UCY.
Session 2 — Regulatory: Panel discussion on U-Space regulation and aviation integration. Led by DCAC and HCAA.

This workshop will position the EUSOME Excellence Hub as a strategic bridge between the European 6G research community and aviation regulation bodies.

This work is supported by the EU Horizon Europe project EUSOME (Grant Agreement No. 101187121).

HCAA publishes a UAS Flight Safety Information Bulletin

Sun, 04 Jan 2026 00:00:00 +0000

The Hellenic Civil Aviation Authority (HCAA) has published a UAS Flight Safety Information Bulletin in order to inform operators and users of Unmanned Aircraft Systems (UAS) about safe and lawful drone operations across Greece. The bulletin has already been distributed to over 12,000 domestic and foreign UAV operators in Greece, with the goal of quickly and broadly distributing vital information for safe operational usage. Especially during the summer period, given the growing frequency of wildfires, particularly in forest areas, HCAA advises that using UAVs in fire-affected areas is illegal unless prior permission and coordination with the appropriate authorities have been secured. Uncontrolled drone activity can seriously interfere with aerial firefighting missions and endanger lives. Particular care is also required in locations with high local winds, which can significantly alter the speed and direction of a fire’s spread, increasing the risk to ground and aircraft firefighting troops. The HCAA has already legalized the use of UAVs for fire prevention and detection, which are operated by Civil Protection at roughly 150 places across Greece, significantly improving the prompt detection and reaction to fires. Within the EUSOME Project, our project partner’s initiative highlights the importance of coordinated actions between authorities, industry, and operators as key enablers for operational safety, and responsible UAS integration within the European drone ecosystem.HCAA encourages all UAV operators and users to thoroughly read the Information Bulletin and consistently execute the instructions and recommendations being contained, therefore contributing to increased safety and environmental protection. https://lnkd.in/gBmiGfzi

Source: EUSOME Project on LinkedIn

Drones and AI in Greece's tax and finance transformation

Fri, 05 Dec 2025 00:00:00 +0000

Greece is undergoing a major digital transformation in its tax and finance sector, led by EUSOME project partner, the Hellenic Ministry of Digital Governance! Leveraging big data, drones, and AI , the country is tackling widespread tax evasion and moving beyond past economic challenges. The new high-tech overhaul, at the heart of the system, is centered on an ultramodern digital center where algorithms analyze millions of real-time transactions across electronic payment, customs, and bank records to flag anomalies with a high potential for illegal activity. Complementing this analysis, drones and live surveillance feeds are deployed to monitor remote and physical locations—from island ports and restaurants to hidden grain silos—providing inspectors with real-time visual evidence to execute targeted raids. This sweeping digitalization effort has been credited with generating a budget surplus, increasing government revenues, and successfully restoring Greece’s sovereign bonds to investment grade. Such developments illustrate how digital governance, automation, and intelligent monitoring technologies are reshaping public-sector efficiency, aligning with the broader European shift toward data-driven decision making and resilient digital infrastructures, a vision central to the EUSOME Project’s mission. Find out more ⬇ : https://lnkd.in/eky9pa5A

Γενική Γραμματεία Τηλεπικοινωνιών και ΤαχυδρομείωνKonstantinos KarantzalosEUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

SocialTech Lab promotes EUSOME at the New European Bauhaus event

Fri, 05 Dec 2025 00:00:00 +0000

Our project partner, the SocialTech Lab, proudly promoted the EUSOME Project at the second edition of the New European Bauhaus in Regions and Cities event, hosted at URsquare and the European Parliament on 29-30 September 2025, in Brussels. More than 400 participants - including NEB Prize finalists, NEB Boost recipients, project promoters, and local and regional authorities - came together to exchange ideas on how to make Europe’s regions more sustainable, inclusive, and beautiful. We were especially proud to see STL’s Social Impact Generator (The Base by CyprusInno) showcased as a finalist in the NEB Prizes 2025 - Champions: Regaining a Sense of Belonging category! Congratulations to our partners for inspiring contributions to Europe’s NEB vision.EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

New Paper: Time series framework for modelling optical LEO satellite links

Mon, 01 Dec 2025 00:00:00 +0000

A novel time series approach is developed to accurately model and predict optical links between Low Earth Orbit (LEO) satellites.

2nd Transnational Plenary Meeting of the EUSOME Project in Patras

Wed, 05 Nov 2025 00:00:00 +0000

The 2nd Transnational Plenary Meeting of the EUSOME Project successfully concluded in Patras on October 8, hosted by our partners at the ATHENA Research Center - Institute of Industrial Systems at the University of Patras. On our first day, the technical partners presented their work so far on the EUSOME vehicle prototypes, the types of sensors to be integrated on them, and the algorithms enabling our progress into autonomy! On the second day, discussions focused on the regulatory aspects for the adoption of U-space in the Southeast Mediterranean region, as well as the communication and dissemination activities completed so far and those planned for the upcoming period. Joining us from a distance, Dr. Constantinos Dovrolis presented the newly established AI Ethics Board - ensuring that the autonomous aspects of the project remain informed by human-centric and interpretable amongst others. The meeting concluded with final remarks from Prof. George Ellinas, project coordinator from KIOS Research and Innovation Center of Excellence of the University of Cyprus, who summarized key outcomes and next steps. Over these two productive days, partners had the opportunity to review progress, exchange insights, and plan the next phase of activities. The discussions were highly engaging, the collaboration inspiring, and the atmosphere truly motivating for everyone involved. Of course, the social dinner was another highlight - a perfect way to celebrate teamwork and strengthen our connections beyond the meeting room with traditional Greek food, music, and dancing! Looking forward to our next gathering and the exciting milestones ahead for EUSOME!!EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

By the time the project is finished we will have our products ready to hit the market

Wed, 05 Nov 2025 00:00:00 +0000

‘By the time the project is finished we will have our products ready to hit the market!’Kicking off the second EUSOME transnational partner meeting this rainy morning in the University of Patras and after a warm welcome from our own Evangelos Vlachos and the Director of the ATHENA Research Center Institute of Industrial Systems Chrysostomos Stylios, we are starting off strong with a presentation from project partner Aviomania Aircraft – our design and construction partners – and Nikos Ioannou explaining the progress on the two EUSOME vehicle designs and production. Stay tuned on our socials today for more key messages from our partners!EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

EUSOME partners at the SkySense 2025 Workshop

Wed, 05 Nov 2025 00:00:00 +0000

On October 6th, EUSOME Project partners joined the Skysense 2025 Workshop, organized within the 11th IEEE Smart Cities Conference (October 6–9, 2025) in Patras. Organized by ATHENA Research Center and the Computer Technology Institute and Press “DIOPHANTUS” (CTI), the workshop featured insightful presentations and discussions from experts of the EUSOME and REFINE projects, exploring how Autonomous Aerial Systems and AI-driven Environmental Monitoring are shaping smart cities and driving urban resilience. Among the speakers were:Afroditi Sakellaropoulou (Hellenic U-space Institute), Christian Vitale and Nicolas Souli (KIOS Research and Innovation Center of Excellence), Christos Skliros (Hellenic Drones S.A.), and Evangelos Vlachos (ATHENA Research Center).A big thank you to all the speakers and organizers for their inspiring contributions and valuable insights! EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Evangelos Vlachos at the SkySense 2025 workshop, University of Patras, 6 October 2025.

Source: EUSOME Project on LinkedIn

Greece is preparing its National Strategy on Unmanned Vehicles

Wed, 05 Nov 2025 00:00:00 +0000

Greece is preparing the National Strategy on Unmanned Vehicles, which was put forward for public consultation earlier this year! This marks a major milestone toward creating a robust, standardized framework to safely and responsibly integrate unmanned vehicles into its national space. The Strategy focuses on four key areas: Establishing clear regulations and guidelines to promote safe usage. Enhancing security and privacy through measures like secure communication and identification. Actively fostering the growth of the domestic drone industry via R&D, investment, and supporting SMEs. Leveraging drone technology for societal benefit in areas like disaster response, environmental monitoring, and other public applications. This important initiative is led by EUSOME Project partner, the Hellenic Ministry of Digital Governance, in close collaboration with the National Authorities and relevant stakeholders. https://lnkd.in/dCNHnfUH

Source: EUSOME Project on LinkedIn

Imagine losing your video link during a BVLOS flight

Wed, 05 Nov 2025 00:00:00 +0000

Imagine losing your video link during a BVLOS flight.‘One of the holy grails of AAM’ Christos Skliros explains, continuing the updates of the EUSOME vehicles in today’s plenary meeting, ‘is the BVLOS flight, the safety assessment of which relies largely on the robustness of the onboard technology.’ Presenting the major considerations in integrating computing and sensors onto the Aviomania Aircraft prototypes, project partner Hellenic Drones S.A. talked to us about selecting sensor types based on environmental conditions, and demonstrated how the flow of the data – sourced from both radar and cameras – is practically processed and displayed as detection instances, in the EUSOME user interface.‘In order to maintain situational awareness, the pilot should have a consistent video link. Imagine that link is lost – you are basically blind in terms of non-cooperative traffic. What we are working on here can be used as a redundancy system that still detects and displays other airspace users – this would allow you to land as soon and as safely as possible.’ Our partners ATHENA Research Center further discussed how this information is brought together in the EUSOME Excellence Hub Infrastructure, focusing on the work required in order to calibrate both sensors in order to be able to project 3D points (radar data), onto the 2D plane (camera data).‘It’s not easy to understand what the radar sees,’ Dimitrios Tsourounis explains, when outlining the challenges of trying to align the manner in which these two different types of information can be used together to paint a clearer picture, ‘we are designing this step-by-step’. Stay tuned on our LinkedIn today and tomorrow for more technical insights as we dive deeper into the technical work of the project!EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Design and Evaluation of a Simulation Framework for FMCW Radar Sensors Deployed on UAV Platforms

Mon, 06 Oct 2025 00:00:00 +0000

ISAC for AAM: Integrated Sensing and Communication Technology for Advanced Air Mobility

Mon, 06 Oct 2025 00:00:00 +0000

New Paper: Design and Evaluation of a Simulation Framework for FMCW Radar Sensors Deployed on UAV Platforms

Mon, 06 Oct 2025 00:00:00 +0000

A novel simulation framework is developed to model and evaluate Frequency Modulated Continuous Wave (FMCW) radar sensors integrated with Unmanned Aerial Vehicle (UAV) platforms.

Publication page

New Paper: ISAC for AAM: Integrated Sensing and Communication Technology for Advanced Air Mobility

Mon, 06 Oct 2025 00:00:00 +0000

Researchers develop an integrated sensing and communication technology to enhance safety and efficiency in advanced air mobility vehicles.

Publication page

The 1st edition of the EUSOME Newsletter has landed

Mon, 06 Oct 2025 00:00:00 +0000

The 1st edition of the EUSOME Newsletter has landed! Subscribe now and be part of our journey toward the future of Advanced Aerial Mobility!EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

New Paper: Improving wideband massive mimo channel estimation with uav state-space information

Wed, 01 Oct 2025 00:00:00 +0000

The paper improves massive MIMO channel estimation by leveraging UAV state-space information to mitigate the effects of beam squint and enhance wideband channel accuracy.

Reached 1200+ Citations on Google Scholar

Wed, 01 Oct 2025 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 1200 times according to Google Scholar.

New Paper: Biconvex Optimization for Time-Domain Channel Estimation Under Dual-Wideband Fading Conditions

Mon, 08 Sep 2025 00:00:00 +0000

This research develops a biconvex optimization approach to estimate time-domain MIMO channel matrices under dual-wideband fading conditions, addressing beam squint and ISI effects in single-carrier point-to-point systems.

New Paper: Time-domain channel estimation for extremely large MIMO THz communication systems under dual-wideband fading conditions

Mon, 08 Sep 2025 00:00:00 +0000

The paper presents a unified time-domain framework for estimating MIMO channel matrices in extremely large THz systems under dual-wideband fading conditions, mitigating beam squint and inter-symbol interference.

Navigating Geo-Zones: Building the Future of Drone Routes in Cyprus

Sat, 06 Sep 2025 00:00:00 +0000

Navigating Geo-Zones: Building the Future of Drone Routes in Cyprus. Designing optimal drone flight corridors is not just about connecting two points—it’s about doing so safely, efficiently, and in full compliance with airspace regulations. In Cyprus, the Department of Civil Aviation has established precise geo-zone rules, especially around airports. Zone A near Larnaca and Paphos is a complete no-fly zone, while Zone B allows restricted operations with altitude limits and additional conditions. These boundaries are essential for protecting manned aircraft operations, but create a complex puzzle for anyone planning drone routes. Within our collaboration with the Department of Civil Aviation, we are tackling this challenge head-on—mapping geo-zones , analyzing stakeholder needs, and using advanced planning tools to propose the safest and most suitable corridors for UAS operations. This is about more than compliance—it’s about building an operational framework where drones can serve logistics, inspections, emergency response, and environmental monitoring without ever compromising safety. The sky is open for innovation—provided we chart the right course. EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

EUSOME at two European events in June 2025

Thu, 07 Aug 2025 00:00:00 +0000

Earlier this June, Professor George Ellinas from the University of Cyprus represented the EUSOME Project at two major European events in Brussels:11-12 June, Horizon Europe Cluster 3 - Civil Security for Society Info Day & Brokerage Event 10-11 June, ERTICO - ITS Europe 1st Annual Partnership Forum Both gatherings brought together experts, policymakers, and industry leaders to discuss the future of mobility, security, and innovation. The Horizon Europe Info Day focused on upcoming funding opportunities and fostered new connections in areas such as disaster resilience, cybersecurity, and border management. The ERTICO Forum presented Europe’s vision for smart, sustainable transport, diving into strategies for automation, connectivity, and public‑private partnerships. By taking part in these events, EUSOME continues to grow its network, explore synergies, and stay aligned with Europe’s evolving landscape in air mobilityand security research, supporting our mission to build resilient, intelligent systems for the future. For more, visit our website: https://lnkd.in/dTCCdahN

Source: EUSOME Project on LinkedIn

Inspiring the Next Generation at the UCY Summer Academies

Thu, 07 Aug 2025 00:00:00 +0000

Inspiring the Next Generation at the UCY Summer Academies On 4 July 2025, Prof. Panayiotis Kolios presented the EUSOME Excellence Hub at the University of Cyprus Summer Academies, engaging with high school and technical school students in the Engineering & Technology Applications Academy. The UCY Summer Academies offer high school students a taste of university life and hands‑on exposure to different fields of study, building inspiration for future academic and career paths. During the session, students were introduced to the Advanced Air Mobility (AAM) concept and the EUSOME Project’s pioneering work on: Safe autonomous flight Drone guidance frameworks City‑wide path planning. These innovations show how new mobility services can reshape both urban and regional environments, making skies safer and smarter. Through this outreach, EUSOME is helping inspire the next generation of researchers and engineers while showcasing the real‑world impact of our work.EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

SocialTech Lab at the WSIS+20 High-Level Event in Geneva

Thu, 07 Aug 2025 00:00:00 +0000

Our partner SocialTech Lab was invited to the WSIS+20 High-Level Event in Geneva (7-10 July), joining over 40 global exhibitors advancing inclusive digital development. In the featured exhibition space, they proudly showcased their work on responsible tech, including our shared EUSOME Project. The World Summit on the Information Society (WSIS Process)+20 Forum High-level Event 2025 welcomed over 11,000 participants from 169 countries onsite, with many more joining virtually. This diverse gathering included 59 ministers and deputies, 56 regulators, more than 60 UN representatives, as well as CEOs, civil society leaders, youth, technical experts, and academics. Stakeholders met to take stock of achievements and key challenges, opportunities, and trends since the Geneva Plan of Action in 2003. The 2025 event served as a platform for discussion of the WSIS+20 review and the WSIS Action Lines. The key takeaways of SocialTech Lab’s participation will contribute to the WSIS+20 Outcome Document 2025, so stay tuned for its release! Photo credits: ©ITU/Pierre AlbouyEUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport, Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, Aviomania Aircraft, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

New Paper: Flexible hybrid precoding for integrated sensing and communications

Tue, 01 Jul 2025 00:00:00 +0000

Researchers propose flexible hybrid precoding schemes to optimize joint sensing and communication performance in resource-constrained wireless systems.

New Paper: Receiving RISs: Enabling channel estimation and autonomous configuration

Tue, 01 Jul 2025 00:00:00 +0000

A novel channel estimation protocol enables autonomous configuration of semi-passive Reconfigurable Intelligent Surfaces for receiving pilot signals in multi-antenna systems, leveraging beamspace sparsity and low-rank properties.

EUSOME at the DEFEA - Defence Exhibition Athens2025

Sun, 08 Jun 2025 00:00:00 +0000

EUSOME at the DEFEA - Defence Exhibition Athens2025! From May 6-8, we had the chance to join one of Europe’s top defense and security events in Athens, Greece. At the Greek Ministry of Development’s exhibition area, our partners from the Industrial Systems Institute / Research Centre ATHENA proudly showcased the capabilities of our millimetre-wave radar technology, a big step forward for autonomous sensing and situational awareness in aerial systems. This cutting-edge innovation is a testament to great teamwork within the EUSOME Project consortium, including the Industrial Systems Institute / Research Centre ATHENA of the ATHENA Research Center and Hellenic Drones S.A.DEFEA 2025 was a fantastic opportunity to connect with industry leaders, defense experts, and innovators from around the world. We’re excited about what’s next and proud to be contributing to the future of Advanced Air Mobility (AAM) in Southeast Europe.EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, N. Karaolides Designs Ltd, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Exciting news from us

Sun, 08 Jun 2025 00:00:00 +0000

Exciting news from us! Tomorrow, on May 20th, within the framework of the EUSOME Project training and mentoring program for the Advanced Air Mobility (AAM) domain, the 1st mentoring visit will take place in Serbia! Led by project partner Centrum Badań Kosmicznych Polskiej Akademii Nauk, this visit aims to assess the training and mentoring needs within the consortium, especially in Serbia, with project partner Institute Mihajlo Pupin. The first visit will lay the foundation for a strong action plan to support skills development, knowledge transfer, and personnel mobility. The program focuses on engaging key Serbian players in accelerating the responsible adoption of AAM technologies, including research institutes, SMEs, and governmental stakeholders. This initiative strengthens collaboration across Europe and supports Serbia’s alignment with the EASA standards. Looking forward to a productive exchange and impactful outcomes! EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, N. Karaolides Designs Ltd, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

KIOS team at the 16th ITS Congress in Seville

Sun, 08 Jun 2025 00:00:00 +0000

The KIOS Research and Innovation Center of Excellence team of the University of Cyprus joined last week, the 16th ITS Congresses event in the vibrant city of Seville! Our partners made the most of the opportunity to connect with mobility experts and share what the EUSOME Project is all about. Special highlight: Prof. Christos Panayiotou, KIOS Director, joined a panel discussion, adding EUSOME’s voice to the wider conversation on connected and sustainable mobility solutions! For more on this exciting congress, visit our website: https://lnkd.in/dQkXzWEA

Source: EUSOME Project on LinkedIn

The EUSOME mentoring and training programme took off in Belgrade

Sun, 08 Jun 2025 00:00:00 +0000

The EUSOME mentoring and training programme officially took off in Belgrade last week! On 20 & 21 May, project partners Emil Wrzosek and Anna K. from the Centrum Badań Kosmicznych Polskiej Akademii Nauk joined the Institute Mihajlo Pupin team to kick off discussions on mentoring needs and collaboration opportunities. Huge thanks to Valentina Janev and Slavica Boštjančič Rakas for their warm welcome and seamless coordination! Day 1 focused on designing a mentoring programme tailored to the Serbian ecosystem, hosted at the IMP Technology Transfer Office Day 2 took the team to the 67th International Fair of Technics and Technical Achievements at the Belgrade Fair, where they had the chance to connect with researchers, universities, and innovative companies shaping Serbia’s tech future. This visit marked an important step in building bridges between institutions and supporting the uptake of Advanced Air Mobility (AAM) and U-space frameworks in the region. We’re more than excited for what’s ahead! Check out some highlights below & stay tuned for more updates soon! https://lnkd.in/dJchgwS3

EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, N. Karaolides Designs Ltd, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

Reached 1100+ Citations on Google Scholar

Thu, 01 May 2025 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 1100 times according to Google Scholar.

Welcome to the EUSOME Project Channel

Wed, 09 Apr 2025 00:00:00 +0000

Welcome to the EUSOME Project Channel! We’re excited to kick things off and share our project with you! EUSOME stands for Enabling Autonomous Aerial Systems through Operational Methods and Technological Enhancements. Funded by European Union’s Horizon Europe programme, EUSOME is a 4-year project, dedicated to advancing Advanced Air Mobility (AAM) in Southeast Europe. What does this mean? We aim to address challenges in transport, emergency response, and climate adaptation to make Advanced Aerial Mobility a real part of everyday life. With 16 partners across 4 countries, we’re working together to push innovation, standardize regulations, and get the public on board with this exciting future. Throwback to our kickoff meeting, when our journey began at the KIOS Research and Innovation Center of Excellence at the University of Cyprus! Over two dynamic days, our team gathered for the first time, exchanged insights, and aligned on the road ahead. Stay tuned - there is so much more to come!EUSOME Project partners: KIOS Research and Innovation Center of Excellence, Hellenic U-space Institute, ATHENA Research Center, Hellenic Ministry of Digital Governance, Cyprus Ministry of Transport Communications and Works, Hellenic Drones S.A., Hellenic Civil Aviation Authority, GRNET - Greek Research & Technology Network, Cyprus Chamber of Commerce & Industry, Adrestia R&D, Centrum Badań Kosmicznych Polskiej Akademii Nauk, SocialTech Lab, Institute Mihajlo Pupin, EPIRUS - Perifereia Ipeirou, N. Karaolides Designs Ltd, and Mediterranean Adventure Ltd.

Source: EUSOME Project on LinkedIn

EUSOME: Excellence Hub for Advanced Air Mobility

Wed, 15 Jan 2025 00:00:00 +0000

Project Overview

EUSOME (Enabling Autonomous Aerial Systems through Operational Methods and Technological Enhancements) is a 4-year European Excellence Hub project funded by the Horizon Europe programme (Grant Agreement No. 101187121).

With a total budget of nearly €6 million, the project aims to modernize the air mobility ecosystem in the Southeast Mediterranean region. It addresses regional challenges such as goods and people transportation, complex landscape surveillance, fire risk management, and traffic congestion monitoring through the integration of large drone systems and intelligent coordination technology.

Core Pillars of Innovation

The project is structured around four primary objectives:

Research & Innovation Infrastructure: Establishing an open-access infrastructure to foster technological advancements in Autonomous Aerial Systems (AAS).
Regulatory Masterplan: Developing a strategic plan for the regional adoption of EASA regulations on Advanced Air Mobility.
Thematic Innovation Hub (TIH): A dedicated hub to explore market possibilities, consultancy services, and open-access policies for project outputs.
Mentoring & Outreach: Building regional expertise through training programs and promoting public engagement to ensure societal acceptance of AAM technology.

My Role

As Principal Investigator for ISI / ATHENA Research Center, I lead the institute’s participation in EUSOME, covering:

6G-integrated U-Space communications — designing ISAC waveforms for UAV swarm coordination and command-and-control links.
UAV path planning under channel uncertainty — developing MPC frameworks that optimize trajectory and MIMO ergodic rates jointly.
Detect-and-Avoid for BVLOS operations — radar-camera fusion for obstacle detection in autonomous UAS.

UAV field test, Athens, January 2026

DEFEA 2025 exhibition, Athens

Getting a UAV ready for flight tests, July 2025

Selected Publications

E. Vlachos, P. Kolios, C. Skliros, “Hardware-Aware SE(3) Control Barrier Functions for Counter-UAS Interceptors with Directed Energy Payloads,” 2026 International Conference on Unmanned Aircraft Systems (ICUAS), Corfu, Greece, June 2026.

The Consortium

Coordinated by the KIOS Research and Innovation Center of Excellence at the University of Cyprus, EUSOME unites 16 partners across Cyprus, Greece, Poland, and Serbia:

Partner	Country	Role
KIOS / University of Cyprus	Cyprus	Coordinator
ATHENA Research Center (ISI)	Greece	Research — PI: E. Vlachos
Hellenic Civil Aviation Authority	Greece	Regulation
Aviomania Aircraft	Greece	Industry
Hellenic Drones S.A.	Greece	Industry
Adrestia R&D	Greece	Research
Ministry of Digital Governance	Greece	Policy
Hellenic U-space Institute	Greece	Standards
GRNET	Greece	Infrastructure
Cyprus Ministry of Transport	Cyprus	Policy
Cyprus Chamber of Commerce	Cyprus	Industry
Mediterranean Adventure Ltd	Cyprus	Operations
Region of Epirus	Greece	Regional authority
SocialTech Lab	Serbia	Outreach
Institute Mihajlo Pupin	Serbia	Research
Centrum Badań Kosmicznych PAN	Poland	Research

Project News & Milestones

September 2026 — SkySense 2026 Workshop (Nicosia): EUSOME co-organised SkySense 2026 with the REFINE project at IEEE CAMAD 2026, including an invited talk by E. Vlachos on cellular ISAC for detecting non-cooperative drones in U-Space.
September 2026 — NextGCom 2026 (Kefalonia): E. Vlachos chaired the special session on integrated sensing and AI-driven communication in 6G perceptive mobile networks and presented EUSOME work on semantically-informed UAV connectivity.
June 2026 — 3rd Plenary Meeting (Nicosia): Consortium-wide review of progress and planning.
June 2026 — ICUAS 2026 (Corfu): EUSOME work on hardware-aware control barrier functions for counter-UAS interceptors was presented at the International Conference on Unmanned Aircraft Systems.
May 2026 — 2nd Press Release: EUSOME published its second press release on the project’s achievements after 15 months of work.
March 2026 — 5G Measurements from the Air: ISI / ATHENA drone flights measured 5G signal strength at 20–60 m altitude over urban areas, to support connectivity-aware UAV mission planning.
February 2026 — eVTOL Design Update: Aviomania Aircraft unveiled design targets for the EUSOME eVTOL platform: under 100 kg empty weight with a modular, mission-adaptable interior.
February 2026 — Science Café on AAM: First public outreach event at the University of Cyprus engaging citizens on Advanced Air Mobility.
February 2026 — 3rd EUSOME Study Visit (Belgrade): Consortium partners gathered at the Institute Mihajlo Pupin for knowledge exchange on U-Space technologies and cross-border coordination.
January 2026 — HCAA UAS Flight Plan Update: The Hellenic Civil Aviation Authority published updated UAS flight plan regulations, advancing the national U-Space framework.
November 2025 — 2nd EUSOME Study Visit (Belgrade): The Institute Mihajlo Pupin hosted the second study visit of the EUSOME Mentoring & Training Programme, with partners from KIOS and CBK PAN and Serbian stakeholders from civil aviation, emergency management and the postal service.
October 2025 — SkySense 2025 Workshop (Patras): EUSOME and the REFINE project co-organised SkySense 2025 at IEEE ISC2 2025, University of Patras (workshop chair: E. Vlachos).
October 2025 — 2nd Transnational Plenary Meeting (Patras): Hosted by ATHENA at the University of Patras; consortium-wide review of progress and planning for the next phase.
October 2025 — Newsletter Launch: First edition of the EUSOME Newsletter published.
September 2025 — Geo-Zone Mapping (Cyprus): Partners working on drone corridor routes and geo-zone infrastructure for national U-Space deployment.
July 2025 — EUSOME at Oshkosh EAA AirVenture: Aviomania Aircraft showcased the EUSOME eVTOL concept at the world’s largest airshow.
July 2025 — UCY Summer Academies: Outreach to the next generation through drone demonstrations and AAM talks at the University of Cyprus.
June 2025 — WSIS+20 High-Level Event: SocialTech Lab represented EUSOME at the World Summit on the Information Society.
May 2025 — EUSOME at DEFEA 2025: Project featured at the Defence Exhibition Athens, demonstrating dual-use potential of AAM technologies.
May 2025 — Mentoring Programme Launch: The EUSOME mentoring and training programme officially kicked off in Belgrade.
January 2025: Official Project Kick-off Meeting in Cyprus.

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101187121.

Biconvex Optimization for Time-Domain Channel Estimation Under Dual-Wideband Fading Conditions

Wed, 01 Jan 2025 00:00:00 +0000

Key Contributions

Development of a unified framework for MIMO channel estimation that explicitly accounts for beam squint at both transmitter and receiver, as well as intersymbol interference (ISI) caused by propagation delays.
Proposal of a novel mixed-integer biconvex optimization model that handles diverse delay patterns using a binary vector and decomposes the channel into path-specific matrices, enabling smart environment integration.
Introduction of a semi-parametric approach that estimates delays and separates path gains/angles, offering advantages over conventional methods and enabling advanced optimization for dual-wideband fading conditions.

Improving Wideband Massive MIMO Channel Estimation With UAV State-Space Information

Wed, 01 Jan 2025 00:00:00 +0000

Key Contributions

Development of a wideband channel model incorporating beam squint effects relevant for high-frequency mMIMO-UAV communications.
Proposal of a state-assisted mMIMO channel estimation approach that leverages UAV motion information to improve estimation accuracy.
Design of an efficient estimation algorithm suitable for single-RF chain architectures, minimizing training overhead.
Demonstration via simulations that the proposed technique achieves high estimation accuracy with significantly reduced training data requirements.

Results & Insights

Simulated UAV flight scenarios and Kalman-based position estimation error across different trajectory profiles.

The state-space model accurately tracks UAV position across diverse scenarios, confirming that UAV motion data can be reliably incorporated into the channel estimation process.

Channel estimation NMSE comparison between the proposed state-assisted method and benchmark schemes across different UAV velocities.

The proposed estimator outperforms benchmark methods, particularly at higher UAV speeds, highlighting the effectiveness of incorporating state-space information for accurate channel estimation.

Intelligent UAV Path Planning for Ergodic Rate Maximization of MIMO Multipath Channels

Wed, 01 Jan 2025 00:00:00 +0000

Key Contributions

Proposes a decentralized MPC framework that jointly optimizes UAV trajectory planning and high-frequency MIMO communication performance under realistic multipath channel conditions.
Integrates ergodic spectral efficiency derived from location-dependent channel models into the control cost function, enabling communication-aware path planning.
Balances three competing objectives — mission time, probabilistic collision safety under stochastic disturbances, and uplink ergodic rate — within a unified control framework.

Flexible Hybrid Precoding for Integrated Sensing and Communications

Sat, 09 Nov 2024 00:00:00 +0000

Key Contributions

Presents flexible hybrid precoding architectures for Integrated Sensing and Communications (ISAC), enabling simultaneous radar sensing and multi-user data transmission from a shared antenna array.
Develops optimization frameworks that balance sensing beampattern design with communication spectral efficiency under practical hardware constraints including finite-resolution phase shifters.
Provides a comprehensive treatment of ISAC precoding in a book chapter context, covering design principles, algorithmic solutions, and trade-off analysis for next-generation wireless systems.

Reached 1000+ Citations on Google Scholar

Sun, 01 Sep 2024 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 1000 times according to Google Scholar.

New Paper: Enabling Extended Reality Applications over 5G Mobile Networks

Thu, 01 Aug 2024 00:00:00 +0000

The study optimizes 5G network performance to support seamless and immersive extended reality experiences on-the-go.

Biography

Mon, 01 Jan 2024 00:00:00 +0000

Dr. Evangelos Vlachos is a Tenure-track Researcher at the Industrial Systems Institute (ISI) of ATHENA RC, where he formed and leads an independent research group on Joint Communication, Control, and Sensing. His research interests include Integrated Sensing and Communications (ISAC), UAV swarm coordination and U-Space resilience, and Scientific Machine Learning (deep unfolding), with a focus on bridging fundamental research with real-world validation in 6G networks and Cyber-Physical Systems.

He has authored 57 publications — including articles in IEEE Transactions on Wireless Communications, IEEE JSTSP, and Proceedings of the Royal Society A — accumulating more than 1,300 citations. His work on graph-based matrix completion received the World’s FIRST 10K Platinum Best Paper Award in 2017, and in 2026 he was awarded a Visiting Fellowship by the University of Jyväskylä, Finland, for a research visit in 2027.

As Principal Investigator, he has secured over €850,000 in competitive and industry funding, including the Horizon Europe Excellence Hub EUSOME and the industry project SPLASH. He maintains active collaborations with Hellenic U-Space Institute, Quantum Neural Technologies, and Hellenic Drones, and has previously collaborated with Huawei Technologies France.

He is a member of the EURASIP Technical Area Committee on Signal Processing for Communications and Networking, and serves as a reviewer for major IEEE journals. He leads the special session “Signal Processing for Integrated Sensing, Communication, and Control in Autonomous Aerial Networks” at ICASSP 2027, chaired special sessions at NextGCom 2026 and IFIP/IEEE VLSI-SoC 2022, and chaired the SkySense 2025 workshop at IEEE ISC2 2025. Recent invited talks include “Cellular ISAC for U-space: Closing the Non-Cooperative Surveillance Gap” at the SkySense workshop of IEEE CAMAD 2026 in Nicosia, and talks at the University of Patras (2026) and the University of Sussex (2022).

He currently teaches at the University of Patras and holds a visiting position at the University of Sussex, with previous postdoctoral and lecturing experience at the University of Edinburgh.

SkySense 2025 workshop, University of Patras, October 2025

Poster presentation at the internal ATHENA RC Day

DroneX Trade Show, ExCeL London, September 2023

Download CV

Contact

Mon, 01 Jan 2024 00:00:00 +0000

Message Sent

Mon, 01 Jan 2024 00:00:00 +0000

Reached 900+ Citations on Google Scholar

Mon, 01 Jan 2024 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 900 times according to Google Scholar.

Teaching

Mon, 01 Jan 2024 00:00:00 +0000

New Paper: Velocity-aided channel estimation for spatially selective mmWave massive MIMO communications

Mon, 04 Sep 2023 00:00:00 +0000

This study introduces velocity-aided channel estimation for spatially selective mmWave massive MIMO communications, significantly improving channel estimation accuracy by incorporating vehicle velocity information.

FIRA-DAV: Feature-level Image and Radar Fusion for Detect and Avoid

Fri, 01 Sep 2023 00:00:00 +0000

Project Overview

FIRA-DAV (Feature-level Image and Radar Fusion for Improved Detect and Avoid Functionality in Autonomous Aerial Vehicles) is a Proof-of-Concept project funded by the Athena Research Center Technology Transfer Office under the internal PoC programme (ΔΕΜΤ, project No. 80260). The project was competitively selected in June 2023 following evaluation by a three-member expert panel, and ran from September 2023 for 6 months.

As Principal Investigator, I led the design, equipment procurement, and dataset construction for radar–camera sensor fusion targeting UAV Detect-and-Avoid (DAA) — a critical safety requirement for beyond-visual-line-of-sight (BVLOS) operations. The project was carried out in collaboration with Hellenic Drones (industry partner, Dr. Christos Skliros) and ROBOSURVEY LTD (equipment supplier).

Team

Dr. Evangelos Vlachos — PI, Athena Research Center / ISI
Hellenic Drones (Dr. Christos Skliros) — Industry partner

Equipment & Dataset

The project procured a Texas Instruments IWR6843AoP 77 GHz FMCW mmWave radar (Antenna-on-Package variant) and a synchronized CSI camera, integrated on a drone-mounted embedded platform. Measurement campaigns produced a synchronized dataset of radar 3D point clouds and RGB camera frames in real flight conditions, including UAV-to-UAV scenarios for obstacle detection.

Key technical work:

Data collection: Synchronized radar point clouds and RGB frames captured in real UAV flight scenarios
Calibration: Radar-to-camera extrinsic transformation, intrinsic camera calibration
CFAR detection: Configuration of detection and tracking layers for the IWR6843AoP sensor
Applied for Google Cloud Research Credits to support deep neural network training on the collected dataset

Technical Outcomes

Synchronized radar–camera dataset collected under real flight conditions
Derivation of transformation function mapping radar 3D point cloud to 2D image plane
Training pipeline for spatial attention fusion neural network combining radar and vision features
Efficient Python and Julia code for embedded AI deployment
Dataset and equipment directly adopted by EUSOME (Horizon Europe, 2025–2028), which carries no equipment budget

Reviewer Recognition

The proposal was rated positively by all three external reviewers:

“This is a huge market and everyone is working on it. This tech, if unique and solves real issues, has cross applicability.”

“Deep-tech IT solution that relies on state-of-the-art technology and strong know-how that is probably difficult to be copied.”

Funded by the Athena Research Center under the internal Proof-of-Concept (PoC) programme (ΔΕΜΤ No. 80260) of the Technology Transfer Office.

SPLASH: Joint Sensing, Communication and Control of Aerial Vehicles

Fri, 01 Sep 2023 00:00:00 +0000

Project Overview

SPLASH (Signal Processing for physicaL-lAyer design of joint Sensing, communication, and control of aerial veHicles) is an industrial research contract between the Industrial Systems Institute of ATHENA RC and the Hellenic U-Space Institute (HUSI). As Principal Investigator, I lead ATHENA’s work on one question: how drones sharing low-altitude airspace can sense their surroundings, keep their communication links and stay under control, using the same radio hardware.

The project started in September 2023 and was extended in 2026.

Research Tasks

Detect-and-avoid with camera and mmWave radar fusion Fusing camera images with mmWave radar detections, so a drone can spot and avoid other aircraft and obstacles in conditions where either sensor alone is unreliable.

Control and communication co-design Designing the flight controller and the wireless link together, so that manoeuvres do not break the link and the link quality is taken into account when the drone plans its motion.

FMCW joint sensing and communication Using FMCW radar waveforms for both sensing and data transmission, so one radio front end serves both functions.

Impact

SPLASH supports HUSI’s U-Space activities, feeds the work of the EUSOME Excellence Hub, and informs ATHENA’s input to ETSI ISG ISAC on integrated sensing and communication for low-altitude airspace.

Funded by the Hellenic U-Space Institute (HUSI) under an industrial research contract (€45,000, 2023–2027).

Reached 800+ Citations on Google Scholar

Sat, 01 Jul 2023 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 800 times according to Google Scholar.

Millimeter Wave Channel Estimation for Lens Based Hybrid MIMO with Low Resolution ADCs

Sun, 28 May 2023 00:00:00 +0000

Presentation

Presenting the paper at IEEE ICC 2023, Rome, 1 June 2023.

New Paper: Covariance-based hybrid beamforming for spectrally efficient joint radar-communications

Sun, 28 May 2023 00:00:00 +0000

This paper develops a covariance-based hybrid beamformer for spectrally efficient joint radar-communications systems, enhancing interference mitigation and spectral efficiency by leveraging fewer RF chains than transmit antennas.

New Paper: Flexible hybrid beamforming for spectrally efficient 6g joint radar-communications

Sun, 28 May 2023 00:00:00 +0000

This research proposes a novel flexible hybrid beamforming scheme for spectrally efficient 6G joint radar-communications, enabling interference mitigation and improved system performance through reduced RF chain usage.

New Paper: Millimeter wave channel estimation for lens based hybrid MIMO with low resolution ADCs

Sun, 28 May 2023 00:00:00 +0000

This paper develops a novel channel estimation method for lens-based hybrid MIMO systems using low-resolution ADCs, significantly improving performance and enabling practical mmWave applications.

Publication page

New Paper: Subset selection based RIS-aided beamforming for joint radar-communications

Wed, 01 Mar 2023 00:00:00 +0000

This research introduces a novel subset selection-based RIS-aided beamforming approach that maximizes spectral efficiency in joint radar-communications systems, enabling more efficient use of RF resources.

Reached 700+ Citations on Google Scholar

Wed, 01 Mar 2023 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 700 times according to Google Scholar.

Covariance-Based Hybrid Beamforming for Spectrally Efficient Joint Radar-Communications

Sun, 01 Jan 2023 00:00:00 +0000

Key Contributions

Proposes a robust hybrid beamformer based on covariance matrices to minimize interference in joint radar-communications systems.
Introduces fractional programming for optimal RF chain selection, enabling flexibility in hybrid beamformer design.
Maximizes mutual information using a weighted dual function approach, considering the trade-off between radar and communication performance.
Incorporates hardware constraints with low-resolution DACs, ensuring practical implementation feasibility.

Ideal communication capacity and its covariance-based approximation for the JRC system.

This figure demonstrates the effectiveness of the proposed covariance-based approach in approximating the ideal communication capacity, highlighting its accuracy and practicality for JRC systems.

Mutual information performance versus SNR for different weighting factors in the JRC system.

The results show that the proposed method consistently outperforms baseline cases, except for the interference-free baseline, across various SNR levels and weighting factors.

Mutual information performance versus SNR for different numbers of transmit antennas in the JRC system.

The proposed method exhibits superior performance compared to baseline approaches, particularly at higher SNR values, indicating its scalability and robustness.

Multi-user interference (MUI) performance versus the number of users for the proposed hybrid beamformer.

The proposed method achieves lower MUI levels compared to baseline approaches, demonstrating its effectiveness in managing interference in multi-user scenarios.

Home

Sun, 01 Jan 2023 00:00:00 +0000

Subset Selection Based RIS-Aided Beamforming for Joint Radar-Communications

Sun, 01 Jan 2023 00:00:00 +0000

Key Contributions

Proposing a subset selection framework for optimizing beamforming weights in RIS-aided joint radar-communication systems.
Developing an efficient algorithm to select the optimal reflecting element subset while considering both radar and communication objectives.
Demonstrating significant performance improvements in terms of radar detection probability and communication throughput compared to conventional methods.

Results & Insights

The proposed algorithm consistently outperforms baseline methods across all SNR regimes, achieving higher detection probability and communication rate.

The algorithm exhibits rapid convergence even for large subset sizes, validating its computational efficiency.

Increasing the reflection coefficient magnitude improves both radar detection probability and communication rate, with diminishing returns beyond a certain threshold.

System performance improves with more reflecting elements, but the rate of improvement diminishes, indicating an optimal configuration size for the given setup.

Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications

Sun, 01 Jan 2023 00:00:00 +0000

Key Contributions

Addresses the challenge of channel estimation in time-varying mmWave massive MIMO channels for moving UAVs, where conventional training-based methods fail due to beam squint and channel dynamics.
Proposes a novel channel estimation technique that leverages the UAV’s velocity information, obtained from its control module, to compensate for the beam squint effect and improve parameter recovery.
Introduces a time-varying channel model specifically designed for mmWave frequencies that incorporates the spatial selectivity (beam squint) effect, enabling more accurate and robust channel state information (CSI) estimation.
Demonstrates the effectiveness of the proposed method in providing reliable channel estimates even with short training sequences, essential for real-time UAV communications.

Results & Insights

This figure shows that the proposed velocity-aided method significantly outperforms conventional training-based approaches in terms of channel estimation accuracy (lower NMSE), especially at higher UAV velocities and moderate to high SNR levels, validating the effectiveness of incorporating velocity information.

The results indicate that the proposed method achieves lower BER for higher data rates and faster UAV speeds compared to baseline methods, demonstrating its practical benefit for reliable mmWave massive MIMO communication with moving platforms.

New Paper: Quantum computing-assisted channel estimation for massive MIMO mmWave systems

Mon, 03 Oct 2022 00:00:00 +0000

This research develops quantum computing-assisted channel estimation techniques to significantly improve computational efficiency in massive MIMO mmWave systems.

Reached 600+ Citations on Google Scholar

Thu, 01 Sep 2022 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 600 times according to Google Scholar.

New Paper: Channel estimation for UAV-based mmwave massive MIMO communications with beam squint

Mon, 29 Aug 2022 00:00:00 +0000

The paper develops a novel channel estimation method for UAV-based mmWave massive MIMO systems with beam squint, improving accuracy by accounting for spatial selectivity in time-varying channels.

New Paper: Regularized Distributed MPC for UAV Networks: Stabilizing Coupled Motion and Hybrid Beam Alignment

Fri, 01 Jul 2022 00:00:00 +0000

Researchers develop a distributed model predictive control approach to stabilize coupled motion and hybrid beam alignment in UAV networks.

New Paper: Optimal mmwave sensor selection for bearing-only localization in smart environments

Tue, 28 Jun 2022 00:00:00 +0000

This research presents a novel optimal sensor selection technique for mmwave-based bearing-only localization in smart environments, improving accuracy and reducing computational complexity.

New Paper: Game-Theoretic Power and Rate Control in IEEE 802.11p Wireless Networks

Thu, 19 May 2022 00:00:00 +0000

Researchers develop game-theoretic algorithms to optimize power and rate control in vehicular networks, improving reliability and efficiency of IEEE 802.11p communication.

New Paper: Admm-based cooperative control for platooning of connected and autonomous vehicles

Mon, 16 May 2022 00:00:00 +0000

This research develops a novel ADMM-based cooperative control framework for platooning of connected autonomous vehicles, enabling efficient and collision-free acceleration adjustments.

New Paper: Green joint radar-communications: RF selection with low resolution DACs and hybrid precoding

Mon, 16 May 2022 00:00:00 +0000

This paper introduces a novel RF chain and DAC bit selection approach for sub-arrayed hybrid MIMO joint radar-communications systems, optimizing energy efficiency while preserving information rate.

New Paper: Towards 6G: Spectrally efficient joint radar and communication with radio frequency selection, interference and hardware impairments

Mon, 25 Apr 2022 00:00:00 +0000

This research contributes to 6G by developing a spectrally efficient joint radar-communication system that selects optimal radio frequencies, mitigates interference and hardware impairments, and achieves higher degrees of freedom.

Reached 500+ Citations on Google Scholar

Fri, 01 Apr 2022 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 500 times according to Google Scholar.

ADMM-based Cooperative Control for Platooning of Connected and Autonomous Vehicles

Sat, 01 Jan 2022 00:00:00 +0000

Key Contributions

Development of a distributed model-predictive control (DMPC) framework for vehicle platooning, incorporating consensus constraints for safety and coordination.
Formulation and solution of the DMPC problem using the Alternating Direction Method of Multipliers (ADMM), enabling low communication overhead and robust performance.
Evaluation of the proposed ADMM-based controller through extensive simulations, demonstrating its effectiveness in maintaining safe inter-vehicle distances and tracking the lead vehicle’s trajectory.

Results & Insights

Trajectories of all vehicles plotted against time, showing the following vehicles’ ability to closely track the leading vehicle’s path.

The simulation results in Fig. 3 demonstrate that the ADMM-based controller enables the platoon to maintain close proximity to the lead vehicle, with minimal tracking error across all vehicles, highlighting the controller’s effectiveness in achieving coordinated motion.

CDF plot of the distance between successive vehicles, illustrating the statistical distribution of inter-vehicle distances under the ADMM controller.

Fig. 5 reveals that the ADMM controller ensures a high probability (100%) of maintaining a safe inter-vehicle distance exceeding 1 meter, whereas other methods like unconstrained MPC and the low-complexity ADMM-L implementation show a significant risk of violating this safety margin.

Green Joint Radar-Communications: RF Selection with Low Resolution DACs and Hybrid Precoding

Sat, 01 Jan 2022 00:00:00 +0000

Key Contributions

Proposed an energy-efficient radio frequency (RF) chain and DAC bit selection approach for sub-arrayed hybrid MIMO joint radar-communication (JRC) systems.
Formulated a fractional energy efficiency (EE) maximization problem to determine the optimal number of RF chains and associated DAC bit resolution for communication scenarios.
Introduced a weighting formulation to represent the combined radar-communications performance, achieving high EE gains and a desirable radar beampattern performance.

Results & Insights

The proposed hybrid precoding approach with low-resolution DACs achieves significantly higher energy efficiency (EE) compared to the fully digital baseline, particularly at lower SNR levels, while maintaining comparable communication rates.

The proposed method achieves a highly directive radar beampattern with minimal sidelobes, comparable to conventional fully-digital systems, even with low-resolution DACs, ensuring effective radar functionality.

The proposed approach exhibits a clear energy-rate trade-off, allowing operators to balance communication throughput and energy consumption by adjusting the radar-communication traffic ratio (ρ), demonstrating its flexibility in practical deployments.

Optimal MmWave Sensor Selection for Bearing-Only Localization in Smart Environments

Sat, 01 Jan 2022 00:00:00 +0000

Key Contributions

Development of an optimal sensor selection technique for mmWave bearing-only localization.
Addressing the challenge of low-cost deployment in smart environments through efficient sensor usage.
Emphasizing privacy-preserving localization methods compared to traditional RF-based approaches.
Providing performance analysis through simulations under various scenarios.

Performance comparison of the proposed optimal sensor selection method versus baseline approaches across different signal-to-noise ratio (SNR) values.

The results demonstrate that the proposed method achieves superior localization accuracy with a reduced number of active sensors, highlighting its efficiency in smart environment applications.

Quantum Computing-Assisted Channel Estimation for Massive MIMO mmWave Systems

Sat, 01 Jan 2022 00:00:00 +0000

Key Contributions

Proposes a quantum computing-assisted framework for channel estimation in massive MIMO mmWave systems, leveraging quantum algorithms to address classical challenges.
Introduces a hybrid approach combining quantum computing with classical signal processing techniques to enhance estimation accuracy and reduce computational complexity.
Demonstrates the potential of quantum algorithms, such as HHL, to solve linear systems arising in channel estimation more efficiently than classical methods.
Provides insights into the practical implementation of quantum-assisted channel estimation, highlighting key performance gains and scalability considerations.

Towards 6G: Spectrally efficient joint radar and communication with radio frequency selection, interference and hardware impairments

Sat, 01 Jan 2022 00:00:00 +0000

Key Contributions

Development of a spectral efficiency maximization approach for inclined joint radar-communication systems with hybrid beamforming.
Design of an efficient selection algorithm for optimizing the number of RF chains while considering interference between radar and communication operations.
Incorporation of hardware impairments in the system model to achieve a practical communication-radar trade-off.

Reached 400+ Citations on Google Scholar

Fri, 01 Oct 2021 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 400 times according to Google Scholar.

New Paper: Joint bit allocation and hybrid beamforming optimization for energy efficient millimeter wave MIMO systems

Mon, 01 Mar 2021 00:00:00 +0000

The paper jointly optimizes digital-to-analog converter bit resolutions and hybrid beamforming matrices to achieve energy-efficient millimeter wave MIMO systems, introducing a novel decomposition of the hybrid precoder and combiner.

Reached 300+ Citations on Google Scholar

Mon, 01 Mar 2021 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 300 times according to Google Scholar.

New Paper: Energy-efficiency maximization of hybrid massive MIMO precoding with random-resolution DACs via RF selection

Mon, 01 Feb 2021 00:00:00 +0000

This research introduces a novel hybrid massive MIMO precoding architecture using random-resolution DACs and RF selection to maximize energy-efficiency while preserving spectral-efficiency.

Energy-Efficiency Maximization of Hybrid Massive MIMO Precoding With Random-Resolution DACs via RF Selection

Fri, 01 Jan 2021 00:00:00 +0000

Key Contributions

Introduces an architecture with low-end components (reduced-resolution DACs) for hybrid massive MIMO precoding to maximize energy efficiency (EE) while minimizing the impact on spectral efficiency (SE).
Proposes a novel analog precoder design where the number of active RF chains is dynamically selected (RF selection) based on a criterion to balance EE and SE.
Develops an optimization framework that jointly designs the analog precoder structure (via RF selection) and the baseband precoding matrix to achieve EE maximization.

Results & Insights

Spectral efficiency (bits/sec/Hz/UE) plotted against transmit power (dBm) for different numbers of active RF chains.

This figure demonstrates that increasing transmit power generally improves spectral efficiency, but the rate of improvement saturates as more RF chains are activated, highlighting the diminishing returns of additional chains.

Energy efficiency (bits/Joule/UE) plotted against the number of transmission streams (Ns).

This plot shows a clear trade-off between energy efficiency and the number of streams; higher energy efficiency is achieved with fewer streams, but the system can support more users or data rates at the cost of reduced EE.

Joint Bit Allocation and Hybrid Beamforming Optimization for Energy Efficient Millimeter Wave MIMO Systems

Fri, 01 Jan 2021 00:00:00 +0000

Key Contributions

Introduces a novel decomposition of hybrid precoders and combiners into three matrices representing the analog precoder/combiner, DAC/ADC bit resolution matrix, and baseband precoder/combiner.
Proposes a joint optimization framework for hybrid beamforming matrices and DAC/ADC bit resolutions to maximize energy efficiency (EE).
Develops an efficient solution based on the alternating direction method of multipliers (ADMM) to solve the optimization problem at both transmitter and receiver.

Results & Insights

System architecture of the mmWave hybrid MIMO system with low-resolution DACs/ADCs, illustrating the three-part decomposition of the hybrid precoder/combiner and the beam training phase.

The proposed three-part decomposition enables joint optimization of analog beamforming, bit resolution allocation, and digital combining, forming the foundation of the energy-efficient end-to-end design.

New Paper: Privacy preservation in industrial IoT via fast adaptive correlation matrix completion

Tue, 01 Dec 2020 00:00:00 +0000

This study develops a fast adaptive method for completing correlation matrices in industrial IoT networks, preserving privacy by adding noise that maximizes uncertainty while maintaining structural information.

New Paper: Radio-frequency chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections

Tue, 01 Dec 2020 00:00:00 +0000

Researchers develop an algorithm to optimize radio-frequency chain selection for efficient wireless transmission while minimizing hardware imperfections.

New Paper: Joint localization and channel estimation for UAV-assisted millimeter wave communications

Sun, 01 Nov 2020 00:00:00 +0000

This research develops a novel joint localization and channel estimation framework for UAV-assisted mmWave communications, enabling accurate positioning and MIMO channel coefficient estimation through beam-space representation and matrix completion.

New Paper: Energy efficiency maximization in millimeter wave hybrid MIMO systems for 5G and beyond

Tue, 27 Oct 2020 00:00:00 +0000

A novel energy-efficient beamformer maximizes millimeter wave hybrid MIMO system performance by optimizing DAC resolution and RF chain selection using Dinkelbach method and subset selection optimization.

Reached 200+ Citations on Google Scholar

Wed, 01 Jul 2020 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 200 times according to Google Scholar.

New Paper: A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation

Fri, 01 May 2020 00:00:00 +0000

A novel hardware architecture for reconfigurable intelligent surfaces minimizes active elements while enabling explicit channel estimation, a crucial step towards programmable wireless environments.

New Paper: Wideband channel tracking for millimeter wave massive MIMO systems with hybrid beamforming reception

Fri, 01 May 2020 00:00:00 +0000

A novel matrix-completion-based approach enables efficient wideband channel tracking in millimeter wave massive MIMO systems with hybrid beamforming reception, reducing training periods while maintaining accurate estimation.

A Hardware Architecture For Reconfigurable Intelligent Surfaces with Minimal Active Elements for Explicit Channel Estimation

Wed, 01 Jan 2020 00:00:00 +0000

Key Contributions

Introduces a novel hardware architecture for reconfigurable intelligent surfaces (RISs) with minimal active elements, utilizing a single RF chain for baseband measurements.
Proposes an alternating optimization approach for explicit channel estimation in the beamspace domain, leveraging the sparsity of wireless channels.
Demonstrates that the proposed architecture enables efficient online RIS configuration for specific environments through reduced training periods.

Results & Insights

The figure shows that the proposed technique achieves superior channel estimation accuracy with fewer training symbols compared to OMP and LS methods, highlighting its efficiency.

The results indicate that while the system achieves a rate of 8.5bps/Hz with perfect channel knowledge, the channel estimation process with T=200 training symbols results in a rate of approximately 7.6bps/Hz, demonstrating a trade-off between estimation accuracy and achievable rate.

Joint Localization and Channel Estimation for UAV-Assisted Millimeter Wave Communications

Wed, 01 Jan 2020 00:00:00 +0000

Key Contributions

Exploiting the line-of-sight (LOS) propagation conditions and beamspace representation of the wireless channel to estimate the UAV position.
Utilizing the estimated UAV position to formulate a matrix completion-based approach for recovering the non-line-of-sight (NLOS) components of the MIMO channel.
Demonstrating superior performance compared to baseline methods, requiring fewer channel training resources.

Results & Insights

NMSE performance of the proposed LOS channel estimation technique versus baseline methods.

The proposed method achieves significantly lower estimation error for the line-of-sight channel component across various scenarios, highlighting its effectiveness in leveraging UAV position information. The results demonstrate that the proposed approach maintains low estimation error even with reduced channel training resources, offering a more efficient solution for joint localization and channel estimation in UAV-assisted mmWave systems.

Radio-frequency chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections

Wed, 01 Jan 2020 00:00:00 +0000

Key Contributions

Development of a framework for hybrid beamforming that explicitly accounts for hardware imperfections, including phase noise and amplitude distortion.
Formulation of an optimization problem to jointly maximize energy efficiency and spectral efficiency, considering the impact of hardware imperfections.
Provision of numerical results demonstrating the performance trade-offs between energy and spectral efficiency under various system configurations and imperfection levels.

Wideband Channel Tracking for Millimeter Wave Massive Mimo Systems with Hybrid Beamforming Reception

Wed, 01 Jan 2020 00:00:00 +0000

Key Contributions

Proposed a matrix-completion-based channel tracking technique for time-correlated wideband mmWave massive MIMO channels with hybrid beamforming reception.
Formulated a constrained multi-objective optimization problem incorporating low-rank and group-sparse properties of the mmWave channel.
Capitalized on a time correlation model to reduce beam training overhead while maintaining estimation accuracy.
Developed an efficient algorithm based on the alternating direction method of multipliers (ADMM) for solving the optimization problem.

Results & Insights

The figure demonstrates that the proposed technique achieves near-optimal performance with significantly reduced training overhead, converging faster and achieving lower NMSE than OMP and AdaOMP for time-varying channels.

The results show that the proposed technique maintains low NMSE and achieves high achievable spectral efficiency even with short training sequences, highlighting its effectiveness in reducing beam training overhead while preserving channel estimation accuracy.

New Paper: Dynamic RF chain selection for energy efficient and low complexity hybrid beamforming in millimeter wave MIMO systems

Sun, 01 Dec 2019 00:00:00 +0000

This paper introduces a novel dynamic RF chain selection framework that maximizes energy efficiency and minimizes complexity in millimeter wave MIMO systems through fractional programming and Dinkelbach method-based optimization.

New Paper: Energy efficient ADC bit allocation and hybrid combining for millimeter wave MIMO systems

Sun, 01 Dec 2019 00:00:00 +0000

This research optimizes ADC bit allocation and hybrid combining in millimeter wave MIMO systems, achieving highly energy-efficient solutions through a novel decomposition approach.

New Paper: Wideband MIMO channel estimation for hybrid beamforming millimeter wave systems via random spatial sampling

Sun, 01 Sep 2019 00:00:00 +0000

A novel random spatial sampling approach enables reliable wideband MIMO channel estimation in hybrid beamforming millimeter wave systems, overcoming hardware limitations and short coherence times.

New Paper: Energy efficient transmission of 3D meshes over mmwave-based massive mimo systems

Mon, 01 Jul 2019 00:00:00 +0000

This research proposes an energy-efficient transmission scheme for 3D meshes over mmWave-based massive MIMO systems, achieving up to 30% reduction in power consumption while maintaining high QoE.

New Paper: Hybrid Beamforming with Random Analog Sampling for Wideband Channel Estimation in Millimeter Wave Massive MIMO Systems

Mon, 01 Jul 2019 00:00:00 +0000

A novel hybrid beamforming technique combining random analog sampling and matrix completion enables accurate wideband channel estimation in millimeter wave massive MIMO systems.

New Paper: Robust and Efficient Privacy Preservation in Industrial IoT via correlation completion and tracking

Mon, 01 Jul 2019 00:00:00 +0000

This research develops a novel approach to efficiently preserve privacy in Industrial IoT systems by leveraging correlation completion and tracking, enabling robust protection against sensitive information leakage.

New Paper: Robust estimator for lens-based hybrid MIMO with low-resolution sampling

Mon, 01 Jul 2019 00:00:00 +0000

This paper presents a robust estimator for lens-based hybrid MIMO systems using low-resolution ADCs, improving symbol estimation accuracy and enabling practical mmWave applications.

New Paper: Signal processing on static and dynamic 3d meshes: Sparse representations and applications

Mon, 01 Jul 2019 00:00:00 +0000

The paper presents novel signal processing techniques for static and dynamic 3D meshes, introducing sparse representations that enable efficient data compression and improved reconstruction accuracy.

Reached 100+ Citations on Google Scholar

Sat, 01 Jun 2019 00:00:00 +0000

The work of Dr. Evangelos Vlachos has now been cited over 100 times according to Google Scholar.

New Paper: Energy efficiency maximization of millimeter wave hybrid MIMO systems with low resolution DACs

Wed, 01 May 2019 00:00:00 +0000

A novel energy-efficient millimeter wave hybrid MIMO beamformer is proposed, maximizing energy efficiency by optimizing DAC resolution and selecting active RF chains using the Dinkelbach method and subset selection optimization.

Publication page

Dynamic RF Chain Selection for Energy Efficient and Low Complexity Hybrid Beamforming in Millimeter Wave MIMO Systems

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Proposing a novel hybrid beamforming architecture with dynamic RF chain selection to optimize energy efficiency.
Introducing a fractional programming framework based on the Dinkelbach method to determine the optimal number of active RF chains.
Developing a low-complexity codebook-based gradient pursuit (GP) algorithm for hybrid precoder design, outperforming state-of-the-art methods like OMP.
Demonstrating significant improvements in energy efficiency and reduced computational complexity compared to brute-force and fully digital beamforming approaches.

Performance comparison of energy efficiency across different hybrid beamforming techniques under varying SNR conditions.

This figure demonstrates that the proposed dynamic RF chain selection framework achieves superior energy efficiency compared to conventional hybrid beamforming methods, particularly at higher SNR levels.

Convergence behavior and bit error rate performance of the proposed Dinkelbach method-based optimization framework.

The results show rapid convergence of the optimization algorithm and significantly lower bit error rates compared to benchmark methods, validating the effectiveness of the proposed approach.

Power consumption analysis of the proposed framework versus conventional hybrid beamforming approaches across different SNR values.

The proposed dynamic RF chain selection reduces power consumption by up to 30% compared to fully-connected hybrid beamforming, especially at moderate SNR levels.

Computational complexity comparison of the proposed gradient pursuit algorithm versus state-of-the-art methods.

The proposed framework achieves substantially lower computational complexity (reduced by 40-60%) while maintaining comparable performance to more complex optimization techniques.

Energy Efficiency Maximization of Millimeter Wave Hybrid MIMO Systems with Low Resolution DACs

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Development of a novel optimization technique combining the Dinkelbach method with subset selection to maximize energy efficiency (EE) in mmWave hybrid MIMO systems with low-resolution DACs.
Consideration of the transmitter-side impact of low-resolution DACs, distinguishing this work from literature primarily focused on receiver-side ADCs.
Formulation of the EE maximization problem under a predefined power budget, accounting for the power consumption of active RF chains and their associated DACs.
Demonstration that the proposed technique achieves near-optimal EE performance with significantly reduced computational complexity compared to an exhaustive search baseline.

Presentation

Presenting the paper at IEEE ICC 2019, Shanghai, May 2019.

Energy Efficient ADC Bit Allocation and Hybrid Combining for Millimeter Wave MIMO Systems

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Introduces a novel decomposition of the hybrid combiner matrix into three parts: analog combiner, bit resolution matrix, and baseband combiner matrix.
Formulates the joint optimization problem as a matrix factorization task to approximate the fully digital combiner matrix.
Proposes an efficient solution using the alternating direction method of multipliers (ADMM) to solve the optimization problem.
Demonstrates that the approach achieves high energy efficiency by optimizing ADC bit allocation and hybrid combining jointly.

Results & Insights

No figures containing numerical results were provided.

Energy Efficient Transmission of 3D Meshes Over MMWave-Based Massive MIMO Systems

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Proposes a complexity-aware perceptual coding scheme for 3D meshes that minimizes reconstruction losses while considering hardware limitations.
Introduces an optimal bit allocation strategy that assigns different quantization resolutions to different levels of detail in the 3D mesh.
Demonstrates significant energy efficiency gains and improved reconstruction quality compared to uniform transmission approaches.

Reconstruction quality comparison between uniform transmission and the proposed bit allocation approach for different 3D meshes and bit-per-vertex values.

This figure shows that the proposed scheme provides superior reconstruction quality, particularly at lower bit rates, by adaptively allocating bits based on perceptual importance.

Comparison of uniform bit allocation versus optimized bit allocation for 3D mesh transmission.

The optimized bit allocation strategy significantly outperforms the uniform approach in terms of reconstruction quality, demonstrating the effectiveness of assigning different bit rates to different mesh details.

Performance (metric 6) versus total power consumption for different transmission approaches.

The results indicate that the proposed scheme achieves substantially better reconstruction quality at lower power levels, highlighting its energy efficiency benefits.

Hybrid Beamforming with Random Analog Sampling for Wideband Channel Estimation in Millimeter Wave Massive MIMO Systems

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Development of a novel wideband channel estimation technique for mmWave massive MIMO systems with hybrid beamforming reception.
Capitalization on matrix completion tools to exploit the sparsity and low-rank properties of wireless channels.
Proposal of an analog combining architecture featuring an extended combiner and random sub-sampler to enhance estimation accuracy.
Demonstration of improved performance in terms of MSE with reduced beam training length.

Results & Insights

This figure demonstrates that the proposed algorithm achieves significantly lower channel estimation error (NMSE) compared to benchmark methods, particularly at higher SNR levels, validating its effectiveness.

The results show that the proposed algorithm converges faster and achieves lower steady-state NMSE even with shorter training lengths, highlighting its efficiency in practical scenarios with limited beam training time.

This plot indicates that the proposed algorithm maintains superior performance even as the number of antenna elements increases, showcasing its scalability for massive MIMO systems.

Robust and Efficient Privacy Preservation in Industrial IoT via correlation completion and tracking

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Proposing a privacy-preserving method for reconstructing the PCA subspace from a subset of correlation values, ensuring that the correlation matrices cannot reveal sensitive information.
Introducing an adaptive matrix completion approach that solves a rank-one completion problem iteratively, addressing the issue of missing entries in distributed IIoT networks.
Demonstrating through extensive simulations that the proposed approach improves privacy preservation, computational efficiency, and robustness against node and link failures.

Results & Insights

The evolution of privacy discrepancy ratio with respect to the number of iterations for K = 20 nodes.

This figure shows that increasing the number of iterations during the reconstruction process significantly enhances privacy preservation, with a notable improvement observed even after a small number of iterations (e.g., 5 iterations). This figure indicates that the obfuscated data covariance matrix is accurately estimated even in sparse networks, demonstrating the effectiveness of the proposed method in maintaining data utility while preserving privacy.

Robust Estimator for Lens-based Hybrid MIMO with Low-Resolution Sampling

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Development of a robust maximum a posteriori (MAP) estimator based on the Expectation-Maximization (EM) algorithm for symbol estimation in lens-based hybrid MIMO systems with low-resolution ADCs.
The proposed estimator effectively mitigates the impairments introduced by low-resolution ADCs and the analog beamformer, improving performance in medium to high signal-to-noise ratio (SNR) regimes.
The work demonstrates that lower ADC resolution does not necessarily degrade system performance, offering a pathway to reduce hardware complexity and power consumption in mmWave MIMO systems.

Results & Insights

Performance comparison of the proposed MAP-EM estimator versus conventional methods in terms of NMSE for different ADC resolutions.

This figure shows that the proposed MAP-EM estimator significantly outperforms conventional methods, particularly at medium to high SNR, across various ADC resolutions, demonstrating its robustness and effectiveness.

Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Comprehensive review of fundamental signal processing models (RPCA, CS, MC) for geometry processing.
Analysis of scalable architectures and optimization algorithms for sparse representation tasks.
Demonstration of sparse modeling impact on 3D processing tasks (noise removal, outlier rejection, data completion).

Performance comparison of different sparse recovery methods on static 3D meshes.

This figure shows that model-based methods outperform traditional approaches in terms of reconstruction accuracy for static meshes.

Comparison of denoising results using PCA-based sparse representation versus conventional methods.

The results demonstrate that the proposed sparse representation method achieves superior feature preservation and noise reduction compared to standard techniques.

Wideband MIMO Channel Estimation for Hybrid Beamforming Millimeter Wave Systems via Random Spatial Sampling

Tue, 01 Jan 2019 00:00:00 +0000

Key Contributions

Development of a novel wideband channel estimation framework for mmWave massive MIMO systems employing hybrid beamforming, addressing the challenges of hardware limitations and short coherence time.
Joint exploitation of the low-rank property of the channel and available angular information to enhance channel recovery accuracy, particularly in short beam training scenarios.
Formulation of the estimation problem as a convex optimization task, leading to a computationally efficient solution via singular value thresholding and leveraging the inherent structure of the mmWave channel.

New Paper: Adaptive windowing for ICI mitigation in vehicular communications

Sat, 01 Dec 2018 00:00:00 +0000

This research introduces adaptive windowing for intercarrier interference mitigation in vehicular communications, improving receiver performance by tracking non-stationary channel statistics.

New Paper: Distributed consolidation of highly incomplete dynamic point clouds based on rank minimization

Sat, 01 Dec 2018 00:00:00 +0000

A novel distributed consolidation approach uses rank minimization to reconstruct highly incomplete dynamic point clouds, enabling reliable generation of 3-D animated models.

New Paper: Massive MIMO channel estimation for millimeter wave systems via matrix completion

Thu, 01 Nov 2018 00:00:00 +0000

This work develops a matrix completion-based approach to massive MIMO channel estimation in millimeter wave systems, achieving reliable recovery with reduced training symbols and improved robustness to channel variability.

New Paper: Efficient channel estimation in millimeter wave hybrid MIMO systems with low resolution ADCs

Sat, 01 Sep 2018 00:00:00 +0000

The proposed algorithm efficiently estimates mmWave MIMO channels using compressed sensing and SURE-based parametric denoising, effectively leveraging low-resolution ADCs in hybrid systems.

New Paper: Energy efficient transmitter with low resolution DACs for massive MIMO with partially connected hybrid architecture

Fri, 01 Jun 2018 00:00:00 +0000

This research optimizes the energy efficiency of millimeter wave massive MIMO systems by designing a transmitter that leverages low-resolution DACs and partially connected hybrid architecture, reducing power consumption while maintaining system performance.

New Paper: Dithered beamforming for channel estimation in mmwave-based massive MIMO

Sun, 01 Apr 2018 00:00:00 +0000

The authors propose dithered beamforming to combat staircase quantization effects in mmWave-based massive MIMO channel estimation, introducing randomness to improve accuracy and robustness.

Adaptive Windowing for ICI Mitigation in Vehicular Communications

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Proposed an adaptive windowing technique for ICI mitigation in OFDM systems operating in vehicular environments.
The technique tracks time-varying channel statistics to maximize average SINR with linear computational complexity.
Demonstrated enhanced robustness to channel estimation errors, which are common in vehicular communication scenarios.

Results & Insights

The proposed technique shows superior ICI mitigation performance, particularly at high SNR, compared to existing fixed and adaptive windowing methods.

The adaptive windowing technique achieves lower bit error rates across various vehicular speeds, confirming its effectiveness in non-stationary channel conditions.

The algorithm demonstrates stable convergence with appropriate step-size selection, maintaining low computational complexity while adapting to channel variations.

The technique exhibits significantly better performance stability in the presence of channel estimation errors compared to conventional methods.

Distributed consolidation of highly-incomplete dynamic point clouds based on rank minimization

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Introduces a novel reconstruction technique based on rank minimization theory to address the challenge of highly incomplete dynamic point clouds, ensuring a unique solution to an otherwise ill-posed problem.
Extends the rank minimization approach to incorporate spatial coherence, which is essential for accurately reconstructing soft-body animations.
Proposes a distributed consolidation method that allows multiple nodes to collaboratively reconstruct point clouds by enforcing temporal coherence between frame clusters, improving scalability and efficiency.

Results & Insights

NMSE comparison of the proposed method against PCA-based reconstruction for different levels of incompleteness.

The results demonstrate that the proposed rank minimization-based method significantly outperforms the PCA-based approach in terms of reconstruction accuracy, particularly for highly incomplete point clouds, achieving lower NMSE across all tested configurations.

Dithered Beamforming for Channel Estimation in Mmwave-Based Massive Mimo

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Proposed a dithered beamforming architecture by injecting random control signals into the analog beamformer and ADCs to combat quantization effects.
Developed a low-complexity Expectation-Maximization algorithm for sparse channel estimation in hybrid beamforming systems with low-resolution ADCs.
Demonstrated that the technique effectively mitigates quantization non-linearities and improves channel estimation performance in mmWave massive MIMO systems.

Hybrid analog-digital receiver with phase dithering in the analog RF combiner and quantization dithering before the ADCs.

This figure shows that the proposed dithered beamforming technique significantly reduces estimation error compared to non-dithered approaches, achieving near-optimal performance with a 1dB improvement for 2- and 3-bit quantization cases. The results demonstrate rapid convergence of the proposed algorithm, approaching near-optimal performance even with an approximate solution for the sparse channel estimation problem.

Efficient Channel Estimation in Millimeter Wave Hybrid MIMO Systems with Low Resolution ADCs

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Proposes an EM-SURE-GAMP algorithm that combines compressed sensing with expectation-maximization and adaptive denoising to exploit channel sparsity.
Develops a solution that adapts to low-resolution ADCs by using an additive quantization noise model (AQNM) and minimizes estimation error through Stein’s Unbiased Risk Estimate (SURE).
Demonstrates significant performance improvements in MSE across various SNR regimes, ADC bit resolutions, and training lengths compared to existing methods like EM-GAMP.

Results & Insights

Mean square error (MSE) versus signal-to-noise ratio (SNR) for different channel estimation methods.

The proposed EM-SURE-GAMP algorithm achieves lower MSE across all SNR regimes compared to the EM-GAMP baseline, particularly at low and high SNR.

Mean square error (MSE) versus the number of ADC bits for different channel estimation methods.

EM-SURE-GAMP consistently outperforms EM-GAMP as the number of ADC bits decreases, demonstrating robustness to quantization effects.

Comparison of channel estimation performance across different training lengths.

The algorithm maintains superior performance even with shorter training sequences, highlighting its efficiency and adaptability to practical constraints.

Illustration of convergence behavior for the EM-SURE-GAMP algorithm.

The EM-SURE-GAMP algorithm converges faster and achieves lower MSE compared to EM-GAMP, indicating its practical suitability for real-time applications.

Energy Efficient Transmitter with Low Resolution DACs for Massive MIMO with Partially Connected Hybrid Architecture

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Formulated an optimization problem to minimize transmitter power consumption by determining the optimal number of RF chains for mmWave massive MIMO systems.
Ensured the system capacity meets a minimum rate requirement while reducing the number of RF chains through a partially connected hybrid architecture.
Demonstrated that the proposed transmitter with low-resolution DACs achieves higher spectral efficiency and up to 30% improved energy efficiency compared to conventional systems with the same number of active RF chains.

Massive MIMO Channel Estimation for Millimeter Wave Systems via Matrix Completion

Mon, 01 Jan 2018 00:00:00 +0000

Key Contributions

Proposes a novel channel estimation algorithm for mmWave massive MIMO systems that jointly exploits both channel sparsity and low-rank properties via matrix completion.
Introduces an ADMM-based iterative optimization approach that converges quickly even for very small training lengths.
Demonstrates superior performance compared to state-of-the-art techniques, achieving better accuracy with significantly shorter training periods.
Enables reliable channel recovery in mmWave systems, which are characterized by high variability and challenging conditions.

Results & Insights

This figure clearly demonstrates the superior performance of the proposed technique across all training lengths, with the proposed method consistently achieving better accuracy than existing methods like SVT and VAMP, even at the smallest training lengths.

The proposed algorithm converges faster and achieves lower NMSE for all training lengths, indicating its efficiency and effectiveness in channel estimation.

This result highlights the proposed technique’s ability to maintain good performance even with very short training sequences, outperforming other methods significantly for all considered training lengths.

New Paper: Efficient graph-based matrix completion on incomplete animated models

Sat, 01 Jul 2017 00:00:00 +0000

Researchers develop an efficient graph-based approach to complete missing data in animated models, leveraging structural relationships between frames.

New Paper: Autonomous driving in 5G: Mitigating interference in OFDM-based vehicular communications

Thu, 01 Jun 2017 00:00:00 +0000

This study develops novel interference mitigation techniques for OFDM-based vehicular communications in 5G networks, enhancing the reliability of autonomous driving applications.

Autonomous driving in 5G: Mitigating interference in OFDM-based vehicular communications

Sun, 01 Jan 2017 00:00:00 +0000

Key Contributions

Development of an OSIC-based interference cancellation solution for OFDM systems in vehicular environments.
Introduction of a CG-based mechanism for efficient equalizer filter estimation, accelerating algorithm convergence.
Demonstration of superior performance compared to existing non-banded ICI cancellation methods, achieving lower BER with reduced complexity.

Results & Insights

Bit Error Rate (BER) performance comparison of the proposed OSIC-based equalizer with existing methods for 4QAM modulation in doubly selective channels.

The results show that the proposed solution achieves significantly lower BER across various signal-to-noise ratio (SNR) conditions compared to existing non-banded ICI cancellation methods, validating its effectiveness in improving communication reliability for V2X systems.

Compressed Sensing for Efficient Encoding of Dense 3D Meshes Using Model-Based Bayesian Learning

Sun, 01 Jan 2017 00:00:00 +0000

Key Contributions

Propose a novel compressed sensing framework for 3D mesh geometry compression using Bayesian learning, enabling aggressive compression ratios without compromising visual quality.
Introduce a multivariate Gaussian model for the most significant graph Fourier transform coefficients of each submesh, derived via expectation-maximization.
Leverage local smoothness of partitioned surfaces to enhance performance in highly underdetermined problems, reducing encoding complexity while maintaining reconstruction accuracy.

Results & Insights

Performance comparison of VNMSE versus compression ratio for the chair model across different methods.

This plot demonstrates the effectiveness of the proposed method, showing competitive compression ratios with significantly lower encoding complexity compared to state-of-the-art approaches, particularly at higher compression ratios.

Efficient graph-based matrix completion on incomplete animated models

Sun, 01 Jan 2017 00:00:00 +0000

Key Contributions

Proposing a graph-based regularization technique for matrix completion in animated models, leveraging structural information for improved accuracy.
Introducing an efficient optimization algorithm that reduces computational complexity while maintaining high reconstruction quality.
Demonstrating the effectiveness of the approach on incomplete animated model data, outperforming baseline methods in terms of accuracy and robustness.

Results & Insights

The proposed method consistently achieves higher accuracy across different datasets, highlighting the effectiveness of the graph-based regularization.

The results indicate that the method maintains low error rates even with significant data missing, showcasing its robustness.

The proposed method converges faster and to a lower error, demonstrating the efficiency of the optimization algorithm.

The method achieves significantly lower computational time, making it suitable for large-scale applications.

The method exhibits superior performance in noisy conditions, maintaining lower error rates compared to alternatives.

New Paper: Compressed sensing for efficient encoding of dense 3D meshes using model-based Bayesian learning

Sun, 01 Jan 2017 00:00:00 +0000

This research develops a model-based Bayesian learning approach to efficiently encode dense 3D meshes using compressed sensing, achieving higher compression ratios while preserving visual quality.

New Paper: Supervised energy disaggregation using dictionary—based modelling of appliance states

Sat, 01 Oct 2016 00:00:00 +0000

A novel supervised energy disaggregation method uses dictionary-based modelling of appliance states to accurately identify and quantify energy consumption patterns in real-world settings.

New Paper: Adaptive completion of the correlation matrix in wireless sensor networks

Mon, 01 Aug 2016 00:00:00 +0000

This research develops an adaptive completion algorithm for wireless sensor networks, preserving privacy while accurately estimating the full-rank correlation matrix.

New Paper: Blind distributed beamforming via matrix completion

Fri, 01 Jul 2016 00:00:00 +0000

This paper introduces a novel matrix completion-based approach to distributed beamforming, enabling efficient maximization of receiver SNR while minimizing transmit power and interference.

New Paper: Low-complexity OSIC equalization for OFDM-based vehicular communications

Fri, 01 Jul 2016 00:00:00 +0000

This paper presents a low-complexity OSIC equalization algorithm for OFDM-based vehicular communications, significantly reducing computational complexity while maintaining robust ICI mitigation performance.

Adaptive completion of the correlation matrix in wireless sensor networks

Fri, 01 Jan 2016 00:00:00 +0000

Key Contributions

Development of a privacy-preserving framework for decentralized correlation matrix estimation in wireless sensor networks.
Proposal of a novel adaptive rank-one matrix completion technique to handle the constrained cooperation between sensor nodes.
Demonstration of significantly faster convergence and lower computational complexity compared to traditional singular value thresholding (SVT) methods.

Convergence of the proposed algorithm showing the estimation error decreasing rapidly over iterations, reaching the ground-truth value after a small number of steps, while the SVT-based method converges much slower.

The results demonstrate that the proposed adaptive completion technique achieves rapid convergence to the true correlation matrix with substantially lower computational overhead, making it particularly suitable for resource-constrained wireless sensor networks with privacy constraints.

Blind distributed beamforming via matrix completion

Fri, 01 Jan 2016 00:00:00 +0000

Key Contributions

Addresses the challenge of distributed beamforming with incomplete channel statistics by leveraging matrix completion techniques.
Proposes a low-complexity iterative method for estimating correlation matrices at the fusion center using partial measurements.
Demonstrates that the technique converges to the optimal beamforming solution with a limited number of relays providing direct communication.

Results & Insights

Convergence curves for matrix completion of correlation matrices R(t) and G(t) versus time, showing NMSE reduction for different numbers of known entries.

These results demonstrate that the proposed matrix completion technique achieves near-optimal performance with significantly fewer known entries (34% for K=10), highlighting its efficiency in recovering the full correlation matrices needed for optimal beamforming.

Separate NMSE convergence of correlation matrices R(t) (top) and G(t) (bottom), comparing full-entry recovery against logarithmic subsampling strategies.

The results show that both matrices converge reliably using only O(K log K) known entries, validating the scalability and practicality of the matrix completion approach for distributed beamforming.

Low-complexity OSIC equalization for OFDM-based Vehicular Communications

Fri, 01 Jan 2016 00:00:00 +0000

Key Contributions

Proposing an OSIC equalization technique tailored for doubly selective vehicular channels to mitigate ICI effectively.
Introducing an iterative Galerkin projection-based algorithm that reduces computational complexity while maintaining performance.
Demonstrating the benefits of the proposed scheme in terms of both performance and complexity through simulations in high Doppler environments.

Results & Insights

Complexity order of the proposed ICI equalization method versus standard vehicular communication standards (V2V, Wi-Fi, WiMAX, LTE, DVB-T) as a function of Doppler spread and OFDM size N.

The proposed Galerkin-based equalizer achieves O(N) complexity for typical vehicular channels, making it significantly more efficient than full ICI cancellation approaches that require O(N²) or O(N⁴) operations.

BER versus SNR comparison of the proposed OSIC equalizer against benchmark schemes for low Doppler spread and 4-QAM modulation.

The proposed method closely tracks the performance of full OSIC while operating at a fraction of the complexity, demonstrating its practical viability for vehicular OFDM systems.

BER versus SNR comparison for medium and high Doppler spread scenarios, illustrating robustness of the proposed equalizer under severe ICI conditions.

The results confirm that the Galerkin projection approach maintains performance gains over banded approximation benchmarks even as Doppler spread increases, validating its suitability for high-mobility environments.

Supervised energy disaggregation using dictionary-based modelling of appliance states

Fri, 01 Jan 2016 00:00:00 +0000

Key Contributions

Proposed a supervised energy disaggregation method using multi-state finite state machines to model appliance behavior.
Introduced a dictionary-based approach where appliance states are represented by power consumption vectors selected via clustering from training data.
Utilized state transition probabilities and a reduced trellis structure to efficiently perform disaggregation during operation.

Results & Insights

The NMSE performance improves with an increasing number of appliances, demonstrating the method’s scalability and effectiveness on real-world data.

The proposed method achieves lower NMSE compared to existing approaches, highlighting its superior accuracy in energy disaggregation.

New Paper: Regularized ICI cancellation in V2V communications

Fri, 25 Sep 2015 00:00:00 +0000

This research develops a novel regularized receiver windowing technique that adapts to non-stationary V2V channel conditions, significantly improving ICI cancellation performance in vehicular environments.

New Paper: Efficient tranceiver [sic] techniques for interference and fading mitigation in wireless communication systems

Tue, 01 Jul 2014 00:00:00 +0000

Researchers develop novel transmitter techniques to mitigate interference and fading effects in wireless communication systems, enhancing reliability and efficiency.

New Paper: A decentralized approach to generalized power system state estimation

Sun, 01 Dec 2013 00:00:00 +0000

This paper presents a decentralized approach to generalized power system state estimation, enabling efficient and scalable joint estimation of network topology and power flow states in large-scale grids.

New Paper: Regularized MMSE ICI equalization for OFDM systems over doubly selective channels

Sun, 01 Dec 2013 00:00:00 +0000

This paper proposes a regularized MMSE ICI equalization scheme for OFDM systems over doubly selective channels, achieving linear complexity and improved performance compared to banded approximations.

New Paper: A scheme for X-ray medical image denoising using sparse representations

Fri, 01 Nov 2013 00:00:00 +0000

This novel scheme leverages sparse representations to denoise X-ray medical images by decomposing noisy images into dictionaries, offering superior denoising results over existing methods.

New Paper: Distributed blind adaptive computation of beamforming weights for relay networks

Sun, 01 Sep 2013 00:00:00 +0000

A distributed, adaptive beamforming approach is developed to maximize fairness in multi-cell relay networks with multiple receive antennas, achieving significant throughput gains.

New Paper: Galerkin projections-based ICI cancellation in OFDM systems with doubly selective channels

Mon, 01 Jul 2013 00:00:00 +0000

This study proposes two low-complexity iterative successive interference cancellation schemes for OFDM systems with doubly selective channels, leveraging Krylov subspace optimization methods to efficiently mitigate inter-carrier interference.

A decentralized approach to generalized power system state estimation

Tue, 01 Jan 2013 00:00:00 +0000

Key Contributions

Development of a decentralized generalized state estimation (GSE) approach that verifies circuit breaker statuses.
Utilization of block-sparsity promoting regularizers to enhance breaker status verification capabilities.
Formulation of the GSE problem as a distributed optimization problem amenable to the alternating direction method of multipliers (ADMM).
Design of a communication-efficient framework that maintains compatibility with existing power system solvers.

The test power system partitioned into four control areas for decentralized state estimation.

This figure demonstrates that the decentralized algorithm achieves estimation accuracy comparable to the centralized solution within a few iterations. The results show that the proposed decentralized `2-regularized GSE significantly reduces the number of incorrect breaker status identifications compared to the ordinary GSE approach.

A scheme for X-ray medical image denoising using sparse representations

Tue, 01 Jan 2013 00:00:00 +0000

Key Contributions

Proposes a novel denoising scheme for X-ray images leveraging sparse and redundant representations over learned dictionaries.
Investigates the impact of different dictionary types (fixed, trained on natural images, trained on X-ray images, trained on noisy images) on denoising performance.
Evaluates the effect of dictionary size and sparsity level on denoising results.

Results & Insights

Comparison of denoising performance across different dictionary types (DCT, general K-SVD, specialized K-SVD, noisy K-SVD) versus noise standard deviation.

This plot demonstrates that specialized and noisy dictionaries generally outperform fixed DCT dictionaries, especially at higher noise levels.

Comparison of denoising performance across different dictionary types (DCT, general K-SVD, specialized K-SVD, noisy K-SVD) versus the number of nonzero coefficients in the representation.

This plot shows that increasing the sparsity level generally improves denoising performance, with the noisy dictionary consistently performing best.

Comparison of different dictionaries in terms of PSNR of the denoised image versus the size of image patch.

This plot indicates that larger patch sizes (e.g., 8x8) generally lead to better denoising performance across all dictionary types.

Comparison of different dictionaries in terms of PSNR of the denoised image versus the number of nonzero elements of the representation vector.

This plot reveals that increasing the number of nonzero elements (sparsity) improves denoising performance, with the noisy dictionary showing the highest PSNR.

Example denoising results showing the improvement in Contrast to Noise Ratio (CNR) and visual quality.

This figure visually demonstrates the effectiveness of the proposed method in enhancing image contrast and revealing finer details like calcifications. This figure provides a qualitative assessment of the denoising performance, clearly showing the enhanced clarity and reduced noise in the reconstructed image.

Distributed blind adaptive computation of beamforming weights for relay networks

Tue, 01 Jan 2013 00:00:00 +0000

Key Contributions

Proposed a distributed blind adaptive algorithm for coordinated beamforming in multi-cell networks with multiple receive antennas.
Designed the algorithm to maximize the harmonic sum of SINR, ensuring fairness among users.
Achieved significant throughput gains over traditional single-cell beamforming schemes.

Results & Insights

The proposed algorithm demonstrates substantial performance improvements, achieving 70% and 47% throughput gains for the 10th percentile and median users, respectively, compared to single-cell beamforming.

The algorithm converges quickly, stabilizing SINR values within a few iterations, ensuring practical implementation in real-time systems.

The proposed algorithm maintains superior performance even as the number of receive antennas increases, unlike existing methods which degrade in large-scale MIMO systems.

Galerkin projections-based ICI cancellation in OFDM systems with doubly selective channels

Tue, 01 Jan 2013 00:00:00 +0000

Key Contributions

Proposing two low-complexity iterative successive interference cancellation (SIC) schemes based on Galerkin projections for ICI cancellation in doubly selective OFDM channels.
Deriving a reduced-rank CG-based SIC technique and improving it using Galerkin projections theory to achieve significant complexity reduction.
Demonstrating through simulations that the proposed schemes offer improved complexity-performance tradeoffs compared to existing methods, particularly for medium Doppler spread scenarios.

Results & Insights

This figure illustrates the effectiveness of the proposed Galerkin projection-based SIC schemes in reducing ICI power across different Doppler spreads, showing superior performance compared to benchmark methods.

The convergence plots demonstrate the fast convergence of the proposed iterative schemes, indicating their practical suitability for real-time OFDM receivers even under challenging channel conditions.

This plot highlights the significant complexity reduction achieved by the proposed Galerkin projection-based schemes, particularly evident when the Doppler spread is moderate compared to the OFDM symbol length.

The Bit Error Rate (BER) results confirm that the proposed schemes maintain high performance levels while drastically reducing computational complexity, outperforming traditional methods in the relevant operating regimes.

Regularized MMSE ICI equalization for OFDM systems over doubly selective channels

Tue, 01 Jan 2013 00:00:00 +0000

Key Contributions

Development of a regularized MMSE ICI equalization technique for doubly selective channels to mitigate the effects of Doppler-induced ICI in OFDM systems.
Proposing a banded approximation for the channel matrix to reduce computational complexity while maintaining performance.
Demonstrating that the regularized estimation can recover performance similar to exact MMSE equalization, offering a balance between performance and computational efficiency.

Results & Insights

Comparison of BER versus SNR for different equalization schemes, showing the performance of the proposed regularized MMSE equalizer.

This figure demonstrates that the proposed regularized MMSE equalizer achieves performance comparable to other advanced methods like iterative MMSE and decision-feedback equalizers, particularly at higher SNR levels.

Comparison of BER versus normalized Doppler frequency for the proposed regularized MMSE equalizer and other methods.

This result highlights the robustness of the proposed regularized MMSE equalizer against frequency-selective fading, showing lower BER across different Doppler conditions compared to the benchmark methods.

Comparison of BER versus the length K of the banded approximation matrix, with SNR fixed at 30 dB.

This figure confirms that increasing the length K improves the BER performance, approaching the performance of the exact MMSE equalizer, while the banded approximation effectively reduces computational complexity.

New Paper: Stochastic gradient pursuit for adaptive equalization of sparse multipath channels

Sat, 01 Sep 2012 00:00:00 +0000

A novel stochastic gradient pursuit algorithm adaptively equalizes sparse multipath channels by exploiting their inherent sparsity and outperforms existing methods in terms of convergence speed.

New Paper: Compressed sensing techniques for decision feedback equalization of sparse wireless channels

Tue, 01 May 2012 00:00:00 +0000

A novel application of compressed sensing techniques significantly improves decision feedback equalization of sparse wireless channels by exploiting filter sparsity.

Compressed Sensing Techniques for Decision Feedback Equalization of Sparse Wireless Channels

Sun, 01 Jan 2012 00:00:00 +0000

Key Contributions

Development of compressed sensing-based decision feedback equalization (DFE) schemes for sparse wireless channels.
Exploitation of channel sparsity to improve the performance of non-sparsity-aware DFE.
Proposing new DFE implementations using basis pursuit and matching pursuit techniques.

Results & Insights

Performance comparison of indirect DFE techniques under different SNR conditions.

The results show that the proposed compressed sensing-based DFE schemes outperform traditional methods, especially at higher SNR levels.

SER performance of direct equalization schemes as a function of training sequence length.

The SER performance improves with longer training sequences, indicating the importance of training for accurate channel estimation.

SER performance of DFE schemes across different SNR values.

The proposed CS-based DFE schemes demonstrate superior performance across all tested SNR levels, confirming their effectiveness.

Stochastic Gradient Pursuit for Adaptive Equalization of Sparse Multipath Channels

Sun, 01 Jan 2012 00:00:00 +0000

Key Contributions

Proposed a novel heuristic algorithm, termed Stochastic Gradient Pursuit, for sparse adaptive equalization.
Utilized a decision-feedback equalization structure to effectively handle long multipath channels.
Exploited channel sparsity from a compressive sensing perspective, developing an extension for unknown sparsity order.
Offered reduced computational complexity while maintaining performance comparable to conventional methods.

Results & Insights

Mean-square error (MSE) learning curves comparing the proposed Stochastic Gradient Pursuit algorithm against several benchmark methods.

These curves demonstrate the fast convergence and improved tracking capabilities of the proposed algorithm compared to conventional and other sparse-aware equalization schemes.

Mean-square error (MSE) learning curves for different sparsity orders in the proposed Stochastic Gradient Pursuit algorithm.

These results show the algorithm’s effectiveness in tracking sparse multipath channels even when the exact sparsity order is unknown.

Bit error rate (BER) performance of the proposed Stochastic Gradient Pursuit algorithm compared to benchmark methods.

The proposed algorithm achieves BER performance comparable to benchmark methods while requiring significantly less computational complexity.

Bit error rate (BER) performance versus input SNR for the proposed Stochastic Gradient Pursuit algorithm.

The results indicate that the proposed algorithm maintains good performance across a range of SNR values, demonstrating its robustness.

New Paper: Greedy algorithms for sparse adaptive decision feedback equalization

Thu, 01 Dec 2011 00:00:00 +0000

This research proposes two novel adaptive decision feedback equalization schemes that exploit sparsity in both channel impulse responses and equalizer filters, leading to improved performance and computational efficiency.

Greedy algorithms for sparse adaptive decision feedback equalization

Sat, 01 Jan 2011 00:00:00 +0000

Key Contributions

Proposing two novel adaptive greedy algorithms for sparse decision feedback equalization (DFE) in communication channels.
Developing a channel estimation-based approach that first identifies significant channel taps using a greedy algorithm and then adaptively computes the equalizer filters.
Demonstrating significantly improved performance in convergence speed, tracking capabilities, and reduced complexity compared to non-sparsity-aware methods and prior sparse-aware algorithms.
Offering a direct adaptive sparse equalizer based on a steepest descent (SD)-based greedy algorithm.

Results & Insights

The results demonstrate that the proposed schemes exhibit significantly improved tracking capabilities when channel parameters change dynamically, as shown in Figure 1(c). The algorithms maintain better performance and adapt faster to the evolving channel conditions compared to existing methods like the one in [7].