Sanusi, Idayat O. and Nasr, Karim M. ORCID: https://orcid.org/0000-0002-8604-6274 (2024) A reinforcement learning approach for D2D spectrum sharing in wireless industrial URLLC networks. IEEE Transactions on Network and Service Management, 21 (5). pp. 5410-5419. ISSN 1932-4537 (Online) (doi:10.1109/TNSM.2024.3445123)

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PDF (Author's Accepted Manuscript) 49834 NASR_A_Reinforcement_Learning_Approach_For_D2D_Spectrum_Sharing_In_Wireless_Industrial_URLLC_Networks_(AAM)_2024.pdf - Accepted Version Download (368kB) | Preview

Abstract

Distributed Radio Resource Management (RRM) solutions are gaining an increasing interest recently, especially when a large number of devices are present as in the case of a wireless industrial network. Self-organisation relying on distributed RRM schemes is envisioned to be one of the key pillars of 5G and beyond Ultra Reliable Low Latency Communication (URLLC) networks. Reinforcement learning is emerging as a powerful distributed technique to facilitate self-organisation. In this paper, spectrum sharing in a Device-to-Device (D2D)-enabled wireless network is investigated, targeting URLLC applications. A distributed scheme denoted as Reinforcement Learning Based Matching (RLBM) which combines reinforcement learning and matching theory, is presented with the aim of achieving an autonomous device-based resource allocation. A distributed local Q-table is used to avoid global information gathering and a stateless Q-learning approach is adopted, therefore reducing requirements for a large state-action mapping. Simulation case studies are used to verify the performance of the presented approach in comparison with other RRM techniques. The presented RLBM approach results in a good trade off of throughput, complexity and signalling overheads while maintaining the target Quality of Service/Experience (QoS/QoE) requirements of the different users in the network.

Item Type:	Article
Additional Information:	“© 2024 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.” - MP
Uncontrolled Keywords:	device-to-device communication, quality of service, resource management, reinforcement learning, throughput, wireless communication, interference, Fifth generation (5G) and beyond wireless networks, radio spectrum management (RRM), distributed algorithms, device-to-device communication (D2D), reinforcement learning, matching theory
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	26 Feb 2025 11:38
URI:	http://gala.gre.ac.uk/id/eprint/49834

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