ISAC | 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.

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.

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.

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.

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.

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.

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).

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

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

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

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.

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).

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: 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.