Paper

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.

This research proposes an energy-efficient transmission scheme for 3D meshes over mmWave-based massive MIMO systems, achieving up to 30% reduction in power consumption while maintaining high QoE.

A novel hybrid beamforming technique combining random analog sampling and matrix completion enables accurate wideband channel estimation in millimeter wave massive MIMO systems.

This research develops a novel approach to efficiently preserve privacy in Industrial IoT systems by leveraging correlation completion and tracking, enabling robust protection against sensitive information leakage.

This paper presents a robust estimator for lens-based hybrid MIMO systems using low-resolution ADCs, improving symbol estimation accuracy and enabling practical mmWave applications.