Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology

Abstract

We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the combined data rate. Both tasks involve non-convex optimization challenges. For the first aim, we devise a sub-optimal analytical approach that focuses on maximizing the desired user's received power, leading to an over-determined system. We also attempt to use approximate solutions utilizing pseudo-inverse (Pinv) and block solution (BLS) based methods. For the second aim, we establish a loose upper bound and employ an exhaustive search (ES). We employ deep reinforcement learning (DRL) to address both aims, demonstrating its effectiveness in complex scenarios. DRL outperforms mathematical approaches for the first aim, with the performance improvement of DDPG over the block solution ranging from 8% to 57.12%, and over the pseudo-inverse ranging from 41% to 190% for a correlation-factor equal to 1. Moreover, DRL closely approximates the ES for the second aim. Furthermore, our findings show that as channel correlation increases, DRL's performance improves, capitalizing on the correlation for enhanced statistical learning.