Paper

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.

A novel energy-efficient millimeter wave hybrid MIMO beamformer is proposed, maximizing energy efficiency by optimizing DAC resolution and selecting active RF chains using the Dinkelbach method and subset selection optimization.

This research introduces adaptive windowing for intercarrier interference mitigation in vehicular communications, improving receiver performance by tracking non-stationary channel statistics.

A novel distributed consolidation approach uses rank minimization to reconstruct highly incomplete dynamic point clouds, enabling reliable generation of 3-D animated models.

This work develops a matrix completion-based approach to massive MIMO channel estimation in millimeter wave systems, achieving reliable recovery with reduced training symbols and improved robustness to channel variability.

The proposed algorithm efficiently estimates mmWave MIMO channels using compressed sensing and SURE-based parametric denoising, effectively leveraging low-resolution ADCs in hybrid systems.