University of Oulu

G. C. Alexandropoulos, K. Stylianopoulos, C. Huang, C. Yuen, M. Bennis and M. Debbah, "Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces," in Proceedings of the IEEE, vol. 110, no. 9, pp. 1494-1525, Sept. 2022, doi: 10.1109/JPROC.2022.3174030

Pervasive machine learning for smart radio environments enabled by reconfigurable intelligent surfaces

Saved in:
Author: Alexandropoulos, George C.1,2; Stylianopoulos, Kyriakos3; Huang, Chongwen4,5,6;
Organizations: 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
2Technology Innovation Institute, Abu Dhabi, United Arab Emirates
3Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
4Author image of Chongwen Huang Chongwen Huang College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
5International Joint Innovation Center, Zhejiang University, Haining, China
6Zhejiang Provincial Key Laboratory of Information Processing, Communications and Networks (IPCAN), Hangzhou, China
7Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design, Tampines, Singapore
8Centre for Wireless Communications, University of Oulu, Oulu, Finland
9CentraleSupélec, University Paris-Saclay, Gif-sur-Yvette, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.2 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-06


The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering 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, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this article, we consider multiuser and multi-RIS-empowered wireless systems and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on deep reinforcement learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth-generation (6G) era are presented along with some key open research challenges. Different from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multiarmed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional deep \(Q\) network (DQN) algorithm, but with lower implementation complexity.

see all

Series: Proceedings of the IEEE
ISSN: 0018-9219
ISSN-E: 1558-2256
ISSN-L: 0018-9219
Volume: 110
Issue: 9
Pages: 1494 - 1525
DOI: 10.1109/jproc.2022.3174030
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the EU H2020 RISE-6G Project under Grant 101017011. The work of Chongwen Huang was supported in part by the China National Key Research and Development Program under Grant2021YFA1000500, in part by the National Natural Science Foundation of China under Grant 62101492, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR22F010002, in part by the National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas), in part by the Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant, in part by the Zhejiang University Education Foundation Qizhen Scholar Foundation, and in part by the Fundamental Research Funds for the Central Universities under Grant 2021FZZX001-21. The work of Chau Yuen was supported by the Singapore Ministry of Education Tier 2 under GrantMOE-000168-01.
Copyright information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.