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.

中文翻译：

---

多跳 RIS 授权的太赫兹通信：基于 DRL 的混合波束成形设计


太赫兹频段 (0.1–10 THz) 的无线通信被设想为未来第六代 (6G) 无线通信系统的关键支持技术之一，其规模将超越大规模多输入多输出 (Massive-MIMO) 技术。然而，太赫兹频率的非常高的传播衰减和分子吸收往往限制了信号传输距离和覆盖范围。受益于最近在实现智能无线电传播环境的可重构智能表面（RIS）方面取得的突破，我们提出了一种用于多跳RIS辅助通信网络的新型混合波束成形方案，以提高太赫兹频段的覆盖范围。特别是，部署多个无源可控RIS来协助基站（BS）和多个单天线用户之间的传输。我们利用深度强化学习 (DRL) 的最新进展来对抗传播损耗，研究了 BS 处的数字波束成形矩阵和 RIS 处的模拟波束成形矩阵的联合设计。为了提高所提出的基于 DRL 的算法的收敛性，设计了两种算法来利用交替优化技术来初始化数字波束形成和模拟波束形成矩阵。仿真结果表明，与基准相比，我们提出的方案能够将太赫兹通信的覆盖范围提高 50%。此外，还表明我们提出的基于 DRL 的方法是解决 NP 难波束形成问题的最先进方法，特别是当 RIS 辅助太赫兹通信网络中的信号经历多跳时。