AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent
Surfaces: Deployment, Prototyping, Experiments, and Opportunities
- URL: http://arxiv.org/abs/2311.04241v1
- Date: Mon, 6 Nov 2023 22:22:00 GMT
- Title: AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent
Surfaces: Deployment, Prototyping, Experiments, and Opportunities
- Authors: Li-Hsiang Shen, Kai-Ten Feng, Ta-Sung Lee, Yuan-Chun Lin, Shih-Cheng
Lin, Chia-Chan Chang, Sheng-Fuh Chang
- Abstract summary: We conduct an intelligent deployment of RIS (i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs associated with an mmWave base station (BS) and a receiver.
The i-Dris can reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with comparably low complexity as well as rapid deployment.
- Score: 9.924381667530971
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The requirement of wireless data demands is increasingly high as the
sixth-generation (6G) technology evolves. Reconfigurable intelligent surface
(RIS) is promisingly deemed to be one of 6G techniques for extending service
coverage, reducing power consumption, and enhancing spectral efficiency. In
this article, we have provided some fundamentals of RIS deployment in theory
and hardware perspectives as well as utilization of artificial intelligence
(AI) and machine learning. We conducted an intelligent deployment of RIS
(i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs
associated with an mmWave base station (BS) and a receiver. The RISs are
deployed on the AGV with configured incident/reflection angles. While, both the
mmWave BS and receiver are associated with an edge server monitoring downlink
packets for obtaining system throughput. We have designed a federated
multi-agent reinforcement learning scheme associated with several AGV-RIS
agents and sub-agents per AGV-RIS consisting of the deployment of position,
height, orientation and elevation angles. The experimental results presented
the stationary measurement in different aspects and scenarios. The i-Dris can
reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with
comparably low complexity as well as rapid deployment, which outperforms the
other existing works. At last, we highlight some opportunities and future
issues in leveraging RIS-empowered wireless communication networks.
Related papers
- Machine Learning for Metasurfaces Design and Their Applications [20.350142630673197]
Machine/deep learning (ML/DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design.
This chapter provides a synopsis of DL techniques for both inverse RIS design and RIS-assisted wireless systems.
arXiv Detail & Related papers (2022-11-02T17:19:37Z) - Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable
Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems.
However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging.
This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
arXiv Detail & Related papers (2022-09-18T03:07:37Z) - 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) - Channel Estimation and Hybrid Architectures for RIS-Assisted
Communications [6.677785070549226]
Reconfigurable intelligent surfaces (RISs) are considered as potential technologies for the upcoming sixth-generation (6G) wireless communication system.
Benefits brought by deploying one or multiple RISs include increased spectrum and energy efficiency, enhanced connectivity, extended communication coverage, reduced complexity at transceivers.
arXiv Detail & Related papers (2021-04-14T20:28:09Z) - 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) - Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial [64.77665786141166]
Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks.
IRS is capable of dynamically altering wireless channels to enhance the communication performance.
Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks.
arXiv Detail & Related papers (2020-07-06T13:59:09Z) - RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment
and Passive Beamforming Design [116.88396201197533]
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS)
The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency.
A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset.
A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
arXiv Detail & Related papers (2020-01-28T14:37:38Z)
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.