Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design (2101.09137v2)

Abstract: 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.

• The paper introduces a RIS-powered multi-hop THz communication framework that integrates DRL for hybrid beamforming.
• The paper develops novel DRL algorithms and initialization techniques that jointly optimize digital beamforming at the BS and analog beamforming at the RIS.
• Simulation results show a 50% improvement in signal range, demonstrating enhanced coverage and reliability for 6G networks.

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

The paper "Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design" addresses the integration of reconfigurable intelligent surfaces (RIS) into terahertz (THz) communication systems to overcome the inherent propagation challenges at THz frequencies. The research explores a hybrid beamforming strategy that utilizes deep reinforcement learning (DRL) to enhance the coverage and reliability of THz-band communications.

Summary of the Research

The paper discusses the potential of the THz frequency band (0.1–10 THz) for sixth-generation (6G) wireless networks, emphasizing its ability to accommodate ultra-high-speed and low-latency communications. However, THz communication systems suffer from significant propagation losses and molecular absorption, which limit their coverage. To mitigate these issues, the paper proposes a multi-hop communication framework utilizing RIS to form a smart radio propagation environment that enhances the coverage range of THz signals. The proposed scheme integrates DRL techniques to optimize the joint design of digital beamforming at the base station (BS) and analog beamforming at the RISs.

Key Contributions

1. Practical Architecture: The paper introduces a practical RIS-based hybrid beamforming architecture. It leverages multiple passive RISs deployed between a BS and multiple single-antenna users to mitigate propagation losses at THz frequencies.
2. Novel Algorithms: The research incorporates DRL to tackle the NP-hard problem of hybrid beamforming design. This approach efficiently addresses the non-convex joint design problem for system performance optimization.
3. Initialization Techniques: The paper presents two methods to initialize beamforming matrices, enhancing the convergence rate of the DRL algorithm and potentially avoiding local optimum traps.
4. Numerical Performance: The simulation results demonstrate the proposed scheme's capability to enhance signal range by 50% compared to benchmarks. The DRL-based method proficiently solves the complex beamforming problem, showing great promise for multi-hop scenarios.

Theoretical and Practical Implications

Theoretically, this work contributes to the advancement of beamforming solutions in environments characterized by high attenuation and multi-user interference, such as THz communications. It showcases the application of model-free machine learning techniques to communication system design, which may inspire further research into exploiting AI for next-generation communication challenges.

Practically, the hybrid beamforming design significantly enhances the viability of THz communications, making it suitable for future mobile networks with extensive coverage and capacity demands. The integration of RIS and DRL into THz systems provides a pathway to scalable and energy-efficient network rollouts, crucial for successful 6G deployments.

Future Directions

This paper opens several avenues for future research and development:

• Enhanced DRL Models: Exploring more advanced DRL models and architectures could yield further improvements in learning convergence and performance under diverse channel conditions.
• RIS Hardware Evolution: Investigating the fabrication and deployment of RIS with enhanced phase control accuracy and multi-functional capabilities could significantly elevate system performance.
• Channel Estimation Techniques: Developing robust channel estimation and tracking algorithms specific to RIS-empowered networks would be critical to harness these architectures fully.

Overall, this paper offers a comprehensive examination of hybrid beamforming strategies in RIS-assisted THz systems, employing innovative DRL techniques to effectively extend the communication range and reliability crucial for future wireless ecosystems.