Publication

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.

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.

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.

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

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.

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

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

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.

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.

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.