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AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: challenges and opportunities

AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: challenges and opportunities
AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: challenges and opportunities

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve physical layer performance, including wireless coverage, achievable data rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-Aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate on centralized, distributed, and hybrid artificial-in-Telligence-Assisted MAC architectures in RIS-Aid-ed multi-user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design.

0163-6804
21-27
Cao, Xuelin
5f8520a8-3869-476c-9a07-8edff001e305
Yang, Bo
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Huang, Chongwen
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Yuen, Chau
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Renzo, Marco di
950fb927-43b2-4b8f-b387-9b0a2e669f15
Han, Zhu
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Niyato, Dusit
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Poor, H. Vincent
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Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Cao, Xuelin
5f8520a8-3869-476c-9a07-8edff001e305
Yang, Bo
25f7291b-230c-4812-98f7-8d617d6fa0f7
Huang, Chongwen
cb95630b-82c2-45c1-959e-b636774b8c61
Yuen, Chau
1b26b32e-5822-4bf8-b39b-2ea02385037d
Renzo, Marco di
950fb927-43b2-4b8f-b387-9b0a2e669f15
Han, Zhu
28e29deb-d470-4165-b198-0923aeac3689
Niyato, Dusit
60fa6dee-78d8-4088-b05c-6d108645ac0c
Poor, H. Vincent
2ce6442b-62db-47b3-8d8e-484e7fad51af
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Cao, Xuelin, Yang, Bo, Huang, Chongwen, Yuen, Chau, Renzo, Marco di, Han, Zhu, Niyato, Dusit, Poor, H. Vincent and Hanzo, Lajos (2021) AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: challenges and opportunities. IEEE Communications Magazine, 59 (6), 21-27, [9475159]. (doi:10.1109/MCOM.001.2001146).

Record type: Article

Abstract

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve physical layer performance, including wireless coverage, achievable data rate, and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-Aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate on centralized, distributed, and hybrid artificial-in-Telligence-Assisted MAC architectures in RIS-Aid-ed multi-user communications systems. Finally, we discuss some challenges, perspectives, and potential applications of RISs as they are related to MAC design.

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Accepted/In Press date: 1 May 2021
e-pub ahead of print date: 1 June 2021
Additional Information: Funding Information: Chau Yuen’s work was supported by A*STAR under its RIE2020 AME IAF-PP (Grant No. A19D6a0053). Chongwen Huang’s work was supported by the Fundamental Research Funds for the Central Universities. Chongwen Huang’s work was supported by the Fundamental Research Funds for the Central Universities, under 2021FZZX001-21. Marco Di Renzo’s work was supported in part by the European Commission through the H2020 ARIADNE project (Grant No. 871464) and H2020 RISE-6G project (Grant No. 101017011). Zhu Han’s work was partially supported by NSF EARS-1839818, CNS1717454, CNS-1731424, and CNS-1702850. Dusit Niyato’s work was partially supported by the National Research Foundation under the AI Singapore Programme (AISG Award No: AISG-GC-2019-003), WASP/NTU grant M4082187 (4080), and Singapore Ministry of Education (MOE) Tier 1 (RG16/20). H. Vincent Poor’s work was supported by U.S. National Science Foundation Grant CCF-1908308. Lajos Hanzo’s work was supported by the European Research Council’s Advanced Fellow Grant QuantCom (Grant No. 789028). Publisher Copyright: © 1979-2012 IEEE.

Identifiers

Local EPrints ID: 448945
URI: http://eprints.soton.ac.uk/id/eprint/448945
ISSN: 0163-6804
PURE UUID: fdec6db2-c631-4d23-9779-fc6f7337b57d
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 11 May 2021 17:10
Last modified: 18 Mar 2024 02:36

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Contributors

Author: Xuelin Cao
Author: Bo Yang
Author: Chongwen Huang
Author: Chau Yuen
Author: Marco di Renzo
Author: Zhu Han
Author: Dusit Niyato
Author: H. Vincent Poor
Author: Lajos Hanzo ORCID iD

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