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Spectrum learning-aided reconfigurable intelligent surfaces for ‘green’ 6G networks

Spectrum learning-aided reconfigurable intelligent surfaces for ‘green’ 6G networks
Spectrum learning-aided reconfigurable intelligent surfaces for ‘green’ 6G networks
In the sixth-generation (6G) era, emerging large scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained. In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize ‘green’ 6G networks. As a remedy, reconfigurable intelligent surfaces (RIS) have been proposed for improving the energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-tointerference-plus-noise ratio (SINR) sometimes may even become degraded. This is because the signals impinging upon an RIS are typically contaminated by interfering signals which are usually dynamic and unknown. To address this issue, ‘learning’ the
properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, termed here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for
intelligently exploiting the inherent characteristics of the radio frequency (RF) spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently ‘think-and-decide’ whether to reflect or not the
incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy-efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.
0890-8044
Yang, Bo
25f7291b-230c-4812-98f7-8d617d6fa0f7
Cao, Xuelin
5f8520a8-3869-476c-9a07-8edff001e305
Huang, Chongwen
cb95630b-82c2-45c1-959e-b636774b8c61
Guan, Yong Liang
b79fbba2-56fe-448f-b421-cab91ee3bfb8
Yuen, Chau
1b26b32e-5822-4bf8-b39b-2ea02385037d
Renzo, Marco Di
851ec05a-0f5d-49b1-aaf6-563604f8b809
Niyato, Dusit
60fa6dee-78d8-4088-b05c-6d108645ac0c
Debbah, Merouane
cda7dfb3-4162-4228-a479-8800143fcb5c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Yang, Bo
25f7291b-230c-4812-98f7-8d617d6fa0f7
Cao, Xuelin
5f8520a8-3869-476c-9a07-8edff001e305
Huang, Chongwen
cb95630b-82c2-45c1-959e-b636774b8c61
Guan, Yong Liang
b79fbba2-56fe-448f-b421-cab91ee3bfb8
Yuen, Chau
1b26b32e-5822-4bf8-b39b-2ea02385037d
Renzo, Marco Di
851ec05a-0f5d-49b1-aaf6-563604f8b809
Niyato, Dusit
60fa6dee-78d8-4088-b05c-6d108645ac0c
Debbah, Merouane
cda7dfb3-4162-4228-a479-8800143fcb5c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Yang, Bo, Cao, Xuelin, Huang, Chongwen, Guan, Yong Liang, Yuen, Chau, Renzo, Marco Di, Niyato, Dusit, Debbah, Merouane and Hanzo, Lajos (2021) Spectrum learning-aided reconfigurable intelligent surfaces for ‘green’ 6G networks. IEEE Network. (In Press)

Record type: Article

Abstract

In the sixth-generation (6G) era, emerging large scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained. In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize ‘green’ 6G networks. As a remedy, reconfigurable intelligent surfaces (RIS) have been proposed for improving the energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-tointerference-plus-noise ratio (SINR) sometimes may even become degraded. This is because the signals impinging upon an RIS are typically contaminated by interfering signals which are usually dynamic and unknown. To address this issue, ‘learning’ the
properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, termed here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for
intelligently exploiting the inherent characteristics of the radio frequency (RF) spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently ‘think-and-decide’ whether to reflect or not the
incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy-efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.

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Accepted/In Press date: 31 August 2021

Identifiers

Local EPrints ID: 451249
URI: http://eprints.soton.ac.uk/id/eprint/451249
ISSN: 0890-8044
PURE UUID: dbc0937e-bedc-4b04-be93-546dd84727df
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 14 Sep 2021 21:00
Last modified: 15 Sep 2021 01:35

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Contributors

Author: Bo Yang
Author: Xuelin Cao
Author: Chongwen Huang
Author: Yong Liang Guan
Author: Chau Yuen
Author: Marco Di Renzo
Author: Dusit Niyato
Author: Merouane Debbah
Author: Lajos Hanzo ORCID iD

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