Paper

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.

The paper presents novel signal processing techniques for static and dynamic 3D meshes, introducing sparse representations that enable efficient data compression and improved reconstruction accuracy.