DRL-Based Energy-Efficient Resource Allocation Frameworks for Uplink NOMA Systems

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Nonorthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and high data-rate requirements of the next-generation networks. In this article, we investigate the joint subchannel assignment and power allocation problem in an uplink multiuser NOMA system to maximize the energy efficiency (EE). Different from conventional model-based resource allocation methods, we propose three deep-reinforcement-learning (DRL)-based frameworks to solve this nonconvex optimization problem, referred to as the discrete DRL-based resource allocation (DDRA) framework, continuous DRL-based resource allocation (CDRA) framework, and joint DRL and optimization resource allocation (DORA) framework. Specifically, for the DDRA framework, a multi-DQN-based network is designed to dynamically allocate resources discretely, which can reduce the output dimension and improve the learning efficiency. To overcome the loss of power discretization in DDRA, a joint DQN and deep deterministic policy-gradient (DDPG)-based network (CDRA framework) is designed to generate the resource allocation policy. The DORA framework is then proposed as a performance boundary. Finally, an event-triggered learning method is combined with all three frameworks to further reduce the computational consumption. The numerical results show that the proposed frameworks can improve the EE performance of the uplink NOMA system and reduce the computation time.