Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multiagent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.