Energy-Efficient NOMA Resource Allocation via Deep Transfer Reinforcement Learning

We propose a multi-agent transfer reinforcement learning (RL) framework for energy-efficient resource allocation in a multi-subband multi-user wireless system using nonorthogonal multiple access (NOMA). It instantiates per-subband and per-user agents from pretrained common agents, and adapts them to service-specific network environments.