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

• URL: http://arxiv.org/abs/2101.09137v1
• Date: Fri, 22 Jan 2021 14:56:28 GMT
• Title: Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
• Authors: Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang, and Merouane Debbah
• Abstract summary: 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. We propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies.
• Score: 39.21220050099642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning [68.63990729719369]
  The wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. This paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals. We develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters.
  arXiv  Detail & Related papers  (2024-10-23T15:36:43Z)
• Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems [17.713210541836155]
  This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user's location and the mmWave channel.
  arXiv  Detail & Related papers  (2022-05-18T08:25:36Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces [115.85072043481414]
  We propose a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs.
  arXiv  Detail & Related papers  (2021-03-31T19:15:49Z)
• Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming [45.257328085051974]
  Wireless communications and sensing at terahertz (THz) band are investigated as promising short-range technologies. Ultra-massive multiple-input multiple-output (UM-MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. We develop THz hybrid beamformers based on both model-based and model-free techniques for a new group-of-subarrays (GoSA) UM-MIMO structure.
  arXiv  Detail & Related papers  (2021-02-27T21:28:34Z)
• Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method [43.95403787396996]
  Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems. We propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies.
  arXiv  Detail & Related papers  (2020-09-20T07:51:49Z)
• Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning [21.770491711632832]
  The reconfigurable intelligent surface (RIS) has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL) The proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-02-24T04:28:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.