Research

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication …

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.

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

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

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.

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.

Distributed model-predictive controllers provide a robust way to adjust the acceleration of each platoon vehicle and avoid collisions. …