Research

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.

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.

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

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.

Intelligent surfaces comprising of cost effective, nearly pas- sive, and reconfigurable unit elements are lately gaining in- creasing …

In this paper, we consider millimeter Wave (mmWave) communications between an Unmanned Aerial Ve- hicle (UA V) and a base station. By …

The next-generation wireless communications require reduced energy consumption, increased data rates and better signal coverage. The …

Millimeter Wave (mmWave) massive Multiple Input Multi- ple Output (MIMO) channel tracking is a challenging task with Hybrid analog and …

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.

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