Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning

• URL: http://arxiv.org/abs/2310.10330v1
• Date: Mon, 16 Oct 2023 12:14:42 GMT
• Title: Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning
• Authors: Guillermo Encinas-Lago, Antonio Albanese, Vincenzo Sciancalepore, Marco Di Renzo, Xavier Costa-P\'erez
• Abstract summary: We present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as DRISA, which trains a DRL agent and, in turn, obtains san optimal RIS deployment. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR) at lower computational time (up to -25 percent) while improving scalability towards denser network deployments.
• Score: 33.98674736140333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G).The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands. However, RISs are not magic bullets. Their employment comes with significant complexity, requiring ad-hoc deployments and management operations to come to fruition. In this paper, we tackle the open problem of bringing RISs to the field, focusing on areas with little or no coverage. In fact, we present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA, which trains a DRL agent and, in turn, obtain san optimal RIS deployment. We validate our framework in the indoor scenario of the Rennes railway station in France, assessing the performance of our algorithm against state-of-the-art (SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR), at lower computational time (up to -25 percent) while improving scalability towards denser network deployments.

Related papers

• Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces [30.1512069754603]
  We propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge. We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system.
  arXiv  Detail & Related papers  (2023-05-18T12:46:42Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• 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)
• Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods [83.98961686408171]
  Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS.
  arXiv  Detail & Related papers  (2022-04-28T09:26:14Z)
• Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network [40.817290717344534]
  Reconfigurable surface (RIS) is an emerging technology for wireless communication systems. We propose to apply a fully convolutional network (WSNFC) to solve this problem. We design a set of channel features that includes both cascaded channels via the RIS and the direct channel.
  arXiv  Detail & Related papers  (2022-01-08T14:16:00Z)
• 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)
• Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design [39.21220050099642]
  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.
  arXiv  Detail & Related papers  (2021-01-22T14:56:28Z)
• Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces [50.622375361505824]
  Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs. We devise low-complexity supervised learning approaches for the RISs' phase configurations.
  arXiv  Detail & Related papers  (2020-10-09T05:35:27Z)
• 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)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)

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.