Reinforcement Learning-Based Resource Allocation for M2M Communications over Cellular Networks

Das, S. K., Rahman, M. S., Mohjazi, L. , Imran, M. A. and Rabie, K. M. (2022) Reinforcement Learning-Based Resource Allocation for M2M Communications over Cellular Networks. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 10-13 Apr 2022, pp. 1473-1478. ISBN 9781665442664 (doi: 10.1109/WCNC51071.2022.9771998)

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The spectrum efficiency can be greatly enhanced by the deployment of machine-to-machine (M2M) communications through cellular networks. Existing resource allocation approaches allocate maximum resource blocks (RBs) for cellular user equipments (CUEs). However, M2M user equipments (MUEs) share the same frequency among themselves within the same tier. This results in generating co-tier interference, which may deteriorate the MUE’s quality-of-service (QoS). To tackle this problem and improve the user experience, in this paper, we propose a novel resource utilization policy, which exploits reinforcement learning (RL) algorithm considering the pointer network (PN). In particular, we design an optimization problem that determines the optimal frequency and power allocation needed to maximize the achievable rate performance of all M2M pairs and CUEs in the network subject to the co-tier interference and QoS constraints. The proposed scheme enables the user equipment (UE) to autonomously select an available channel and optimal power to maximize the network capacity and spectrum efficiency while minimizing co-tier interference. Moreover, the proposed scheme is compared with traditional spectrum allocation schemes. Simulation results demonstrate the superiority of the proposed scheme than that of the traditional schemes. Moreover, the convergence of the proposed scheme is investigated which reduces the computational complexity (CC).

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Mohjazi, Dr Lina
Authors: Das, S. K., Rahman, M. S., Mohjazi, L., Imran, M. A., and Rabie, K. M.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Published Online:16 May 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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