Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Das, S. K., Benkhelifa, F., Sun, Y. , Abumarshoud, H. , Abbasi, Q. H. , Imran, M. A. and Mohjazi, L. (2023) Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges. Computer Networks, 224, 109581. (doi: 10.1016/j.comnet.2023.109581)

Abstract

Sixth generation (6G) internet of things (IoT) net-works will modernize the applications and satisfy user de-mands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable in-telligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradi-ent (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Sun, Dr Yao and Abumarshoud, Dr Hanaa and Mohjazi, Dr Lina and Abbasi, Professor Qammer
• Authors: Das, S. K., Benkhelifa, F., Sun, Y., Abumarshoud, H., Abbasi, Q. H., Imran, M. A., and Mohjazi, L.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Computer Networks
• Publisher: Elsevier
• ISSN: 1389-1286
• ISSN (Online): 1872-7069
• Published Online: 21 January 2023
• Copyright Holders: Copyright © 2023 Elsevier B.V.
• First Published: First published in Computer Networks 224: 109581
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher