Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162700
Title: Spectrum-learning-aided reconfigurable intelligent surfaces for 'Green' 6G networks
Authors: Yang, Bo
Cao, Xuelin
Huang, Chongwen
Guan, Yong Liang
Yuen, Chau
Di Renzo, Marco
Niyato, Dusit
Debbah, Mérouane
Hanzo, Lajos
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yang, B., Cao, X., Huang, C., Guan, Y. L., Yuen, C., Di Renzo, M., Niyato, D., Debbah, M. & Hanzo, L. (2021). Spectrum-learning-aided reconfigurable intelligent surfaces for 'Green' 6G networks. IEEE Network, 35(6), 20-26. https://dx.doi.org/10.1109/MNET.110.2100301
Project: Tier 2 MOE000168-01
A19D6a0053 
AISG2-RP-2020-019
M4082187 (4080) 
MOE Tier 1 (RG16/20)
Journal: IEEE Network
Abstract: In the sixth generation (6G) era, emerging large-scale computing-based applications (e.g., processing enormous amounts of images in real time in autonomous driving) tend to lead to excessive energy consumption for 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 (RISs) have been proposed for improving energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-interference-plus-noise ratio (SINR) sometimes may become degraded. This is because the signals impinging on an RIS are typically contaminated by interfering signals that 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, referred to 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 spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently 'thinking and deciding' whether or not to reflect 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.
URI: https://hdl.handle.net/10356/162700
ISSN: 0890-8044
DOI: 10.1109/MNET.110.2100301
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

SCOPUSTM   
Citations 50

3
Updated on Nov 18, 2022

Web of ScienceTM
Citations 50

2
Updated on Nov 19, 2022

Page view(s)

17
Updated on Nov 25, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.