Research

The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog - cloud computing …

It is well known that large antenna arrays with beamforming capabilities are required to compensate for the high path-loss at …

Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, …

Millimeter Wave (mmWave) massive Multiple In- put Multiple Output (MIMO) systems realizing directive com- munication over large …

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.

This research optimizes the energy efficiency of millimeter wave massive MIMO systems by designing a transmitter that leverages low-resolution DACs and partially connected hybrid architecture, reducing power consumption while maintaining system performance.

The authors propose dithered beamforming to combat staircase quantization effects in mmWave-based massive MIMO channel estimation, introducing randomness to improve accuracy and robustness.