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

• Published in: IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1473 - 1478
• Main Authors: Das, Sree Krishna, Siddikur Rahman, Md, Mohjazi, Lina, Imran, Muhammad Ali, Rabie, Khaled M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.04.2022
• Subjects: Cellular networks; Interference; M2M communications; Machine-to-machine communications; pointer network; Quality of service; reinforcement learning; resource allocation; Simulation; System performance; Throughput
• ISSN: 1558-2612
• DOI: 10.1109/WCNC51071.2022.9771998

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).