Twin Delayed DRL Approach for Resource Allocation in Multi-User NOMA Systems

Nonorthogonal multiple access (NOMA) technology shows the potential for improving spectral efficiency and enables massive connectivity in future wireless networks. Unlike orthogonal schemes that require separate resources for each user, NOMA allows multiple users to share the same frequency and time resource. However, joint subchannel assignment and power allocation in multiuser uplink NOMA systems is NP-hard to solve, posing a significant challenge. In this paper, we formulate this joint problem to maximize the energy efficiency and propose a deep reinforcement learning-based approach as a solution. In this approach, we adopt the twin delayed deep deterministic algorithm for the power allocation and deep Q network for the subchannel assignment. Simulation results demonstrate that the proposed approach improves the energy efficiency performance of the multiuser uplink NOMA system and outperforms other methods.