Paper

This research introduces a novel hybrid massive MIMO precoding architecture using random-resolution DACs and RF selection to maximize energy-efficiency while preserving spectral-efficiency.

This study develops a fast adaptive method for completing correlation matrices in industrial IoT networks, preserving privacy by adding noise that maximizes uncertainty while maintaining structural information.

Researchers develop an algorithm to optimize radio-frequency chain selection for efficient wireless transmission while minimizing hardware imperfections.

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.

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.

A novel random spatial sampling approach enables reliable wideband MIMO channel estimation in hybrid beamforming millimeter wave systems, overcoming hardware limitations and short coherence times.