Sparse signal processing for terahertz communication systems

Abstract

The transition to 6G and beyond necessitates the exploration of the terahertz (THz) frequency band, which holds the promise of enabling ultra-high data rates, low latency, and massive connectivity. However, the deployment of THz communication systems is challenged by significant technical barriers, including severe path loss, high atmospheric molecular absorption, hardware impairments, and complex propagation environments. This thesis addresses these challenges through the development of advanced sparse signal processing techniques specifically tailored for THz communication systems, focusing on compressed sensing (CS), intelligent reflecting surfaces (IRS), and deep learning-based signal recovery models.