Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design

By Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang, Merouane Debbah. IEEE Journal on Selected Areas in Communications, 2021

401 citations recorded by the source corpus.

Wireless communication in the TeraHertz band (0.1–10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

View Paper: https://www.semanticscholar.org/paper/d0216350d46afca00869c5822093ebf4d89adc71

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “ML methods need to centrally train data on a specific data center [2], [3], [4], [5], [6], [7].”
Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade (2021, result): “For example, simulation results illustrate the feasibility and advancement of multi-hop THz RIS assisted networks, compared to single-hop systems and those without RIS [249].”
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions (2022, background): “…a high accuracy and fast convergence of the RNN. Enlarging signal propagation distance and coverage range in THz communications, the authors of [263] deploy multiple passive HMIMOS for assisting signal transmissions between a BS and single-antenna UEs, in which they propose a deep RL based…”
An Overview of Signal Processing Techniques for Terahertz Communications (2020, background): “Furthermore, deep reinforcement learning for IRS-based hybrid beamforming is studied in [425], [426]”