Cache‐enabled reinforcement learning scheme for power allocation and user selection in opportunistic downlink NOMA transmissions

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  • Ahmad Gendia
    Graduate School of Information Science and Electrical Engineering Kyushu University 744 Motooka, Nishi‐ku, Fukuoka Japan 819‐0395
  • Osamu Muta
    Center for Japan‐Egypt Cooperation in Science and Technology, Kyushu University 744 Motooka, Nishi‐ku Fukuoka Japan 819‐0395
  • Ahmed Nasser
    Department of Electronics and Communications, Faculty of Engineering Suez Canal University 4.5 Km, Ring Road Ismailia Egypt 41522

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<jats:p>Non‐orthogonal multiple access (NOMA) allows multiple user equipment (UE) to simultaneously share the same resource blocks using varying levels of transmit power at the base station (BS) side. Proper allocation of transmission power and selection of candidate users for pairing over the same resource block are critical for an efficient utilization of the available resources. Optimal UE selection and power splitting among paired UEs can be made through an exhaustive search over the space of all possible solutions. However, the cost incurred by such approach can render it practically infeasible. Reinforcement learning (RL) deploying double deep‐Q networks (DDQN) is a promising framework that can be adopted for tackling the problem. In this article, an RL‐based DDQN scheme is proposed for user pairing in opportunistic access to downlink NOMA systems with capacity‐limited backhaul link connections. The proposed algorithm relies on proactive data caching to alleviate the throttling caused by backhaul bottlenecks, and optimized UE selection and power allocation are accomplished through the continuous interaction between an RL agent and the NOMA environment to increase the overall system throughput. Simulation results are presented to showcase the near‐optimal strategy achieved by the proposed scheme. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.</jats:p>

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