University of Oulu

K. Stylianopoulos, G. Alexandropoulos, C. Huang, C. Yuen, M. Bennis and M. Debbah, "Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISs," ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, Republic of, 2022, pp. 1556-1561, doi: 10.1109/ICC45855.2022.9838369

Deep contextual bandits for orchestrating multi-user MISO systems with multiple RISs

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Author: Stylianopoulos, Kyriakos1; Alexandropoulos, George1; Huang, Chongwen2;
Organizations: 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece
2College of Information Science and Electronic Engineering, Zhejiang University, China
3Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore
4Centre for Wireless Communications, University of Oulu, Finland
5Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-02-10


The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from the field of Deep Reinforcement Learning (DRL) have recently gained popularity in maximizing the sum-rate performance in multi-user communication systems empowered by RISs. Such approaches are commonly based on Markov Decision Processes (MDPs). In this paper, we instead investigate the sum-rate design problem under the scope of the Multi-Armed Bandits (MAB) setting, which is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB formulation is more appropriate to the channel and system models under the assumptions typically made in the RIS literature. To this end, we propose a simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered multi-user Multiple-Input Single-Output (MISO) systems, which we numerically show to perform equally well with a state-of-the-art MDP-based approach, while being less demanding computationally.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-5386-8347-7
ISBN Print: 978-1-5386-8348-4
Pages: 1556 - 1561
DOI: 10.1109/icc45855.2022.9838369
Host publication: ICC 2022 - IEEE International Conference on Communications
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been supported by the EU H2020 RISE-6G project under grant number 10101701 and by MOE Tier 2 MOE-000168-0
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