# Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design

arXiv: https://arxiv.org/abs/2101.09137 · HTML: https://mlsanity.com/paper/2101.09137/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 67 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 30 of 37)

- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic](https://mlsanity.com/paper/1611.02247/) (2016)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)
- [Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.06146/) (2018)
- [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019)
- [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019)
- [Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination](https://mlsanity.com/paper/1907.00123/) (2019)
- [A Speculative Study on 6G](https://mlsanity.com/paper/1902.06700/) (2019)
- [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019)
- [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [Unsupervised Learning for Passive Beamforming](https://mlsanity.com/paper/2001.02348/) (2020)
- [Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems](https://mlsanity.com/paper/1905.13212/) (2019)
- [A New Wireless Communication Paradigm through Software-controlled Metasurfaces](https://mlsanity.com/paper/1806.01792/) (2018)
- [An Overview of Signal Processing Techniques for Terahertz Communications](https://mlsanity.com/paper/2005.13176/) (2020)
- [Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design](https://mlsanity.com/paper/1809.01423/) (2018)
- [Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization](https://mlsanity.com/paper/1909.10462/) (2019)
- [Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization](https://mlsanity.com/paper/2002.10112/) (2020)
- [Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization](https://mlsanity.com/paper/1906.06021/) (2019)
- [Combating the Distance Problem in the Millimeter Wave and Terahertz Frequency Bands](https://mlsanity.com/paper/1902.04682/) (2019)
- [An Interpretable Neural Network for Configuring Programmable Wireless Environments](https://mlsanity.com/paper/1905.02495/) (2019)
- [Parallel Factor Decomposition Channel Estimation in RIS-Assisted Multi-User MISO Communication](https://mlsanity.com/paper/2001.09413/) (2020)
- [Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface](https://mlsanity.com/paper/2002.07063/) (2020)
- [Secure Transmission for Intelligent Reflecting Surface-Assisted mmWave and Terahertz Systems](https://mlsanity.com/paper/2005.13451/) (2020)

## Built on by (most cited first, 10 of 10)

- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities](https://mlsanity.com/paper/2311.04241/) (2023)
- [Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control](https://mlsanity.com/paper/2211.02296/) (2022)
- [Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.14152/) (2022)
- [A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications](https://mlsanity.com/paper/2309.05343/) (2023)
- [RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility](https://mlsanity.com/paper/2403.05133/) (2024)
- [Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS](https://mlsanity.com/paper/2409.11270/) (2024)
- [RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization](https://mlsanity.com/paper/2512.18788/) (2025)
- [Over-the-Air Goal-Oriented Communications](https://mlsanity.com/paper/2512.20533/) (2025)
- [Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning](https://mlsanity.com/paper/2602.13238/) (2026)

## Abstract (arXiv)

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.