A machine learning approach for resource allocation in wireless industrial environments
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Sanusi, Idayat O. and Nasr, Karim M. 
ORCID: https://orcid.org/0000-0002-8604-6274
(2022)
A machine learning approach for resource allocation in wireless industrial environments.
In: Eighteenth Advanced International Conference on Telecommunications (AICT), 26th - 30th June 2022, Porto, Portugal.
International Academy, Research, and Industry Association (IARIA) - Advanced International Conference on Telecommunications (AICT), Wilmington DE; Red Hook NY, USA, pp. 18-23.
ISBN 978-1612089560
ISSN 2308-4030 (Online)
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49840 NASR_A_Machine_Learning_Approach_For_Resource_Allocation_In_Wireless_Industrial_Environments_(AAM)_2022.pdf - Accepted Version Download (207kB) | Preview |
Abstract
In this paper, we present a machine learning technique for channel selection in a Device to Device (D2D)-enabled cellular network targeting a wireless industrial environment. The presented Base Station Assisted (BSA) reinforcement learning technique uses a distributed local Q-table for the agents (users), to prevent global information gathering within the cellular network. A stateless Q-learning approach is adopted to reduce the complexity of learning and the dimension of the Q-table. After the training of the D2D agents, the Q-tables of the D2D users are uploaded to the base station for resource allocation to be implemented centrally. Simulations results show that the presented technique provides a Radio Resource Management (RRM) solution with a good Quality of Service (QoS) performance compared to other conventional approaches.
| Item Type: | Conference Proceedings |
|---|---|
| Title of Proceedings: | Eighteenth Advanced International Conference on Telecommunications (AICT), 26th - 30th June 2022, Porto, Portugal |
| Uncontrolled Keywords: | 5G and beyond networks, radio resource management, distributed algorithms, Device-to-Device communication, 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: | |
| Last Modified: | 26 Feb 2025 13:06 |
| URI: | http://gala.gre.ac.uk/id/eprint/49840 |
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