Paper

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.

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.

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

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.

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

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