Sanusi, Idayat O. and Nasr, Karim M. ORCID: https://orcid.org/0000-0002-8604-6274 (2022) Base Station Assisted (BSA) reinforcement learning for resource allocation in wireless industrial environments. International Journal On Advances in Telecommunications, 15 (3 & 4). pp. 60-69. ISSN 1942-2601

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Abstract

Device-to-Device (D2D) enabled cellular networks are a promising solution for Ultra-Reliable Low-Latency Communication (URLLC) systems. Integrating D2D into future wireless industrial networks and next-generation manufacturing can support the creation of massive machine-type wireless connections. In this paper, we present a Base Station Assisted (BSA) reinforcement learning approach for resource allocation in a D2D-enabled cellular network targeting smart manufacturing and Industry 4.0 applications. A distributed local Q-table is used for the D2D agents to prevent global information gathering and a stateless Q-learning approach is adopted to reduce the complexity of learning and the dimension of the Q-table. The Q-tables of the D2D agents are then uploaded to the Base Station (BS) for the resource allocation to be implemented centrally. Simulation case studies show that the presented semi distributed BSA technique results in reduced signalling overheads and a good Quality of Service (QoS) across the network compared to other conventional schemes.

Item Type:	Article
Additional Information:	NOTE: Starting with 2023, this journal has been merged with the International Journal on Advances in Networks and Services. - MP
Uncontrolled Keywords:	Fifth Generation (5G) and beyond networks, Radio Resource Management (RRM), distributed algorithms, Device-to-Device communication (D2D), reinforcement, learning
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)
Related URLs:	Publisher
Last Modified:	26 Feb 2025 12:45
URI:	http://gala.gre.ac.uk/id/eprint/49839

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