NOMA-ISAC Empowered UAV Networks: Joint Clustering and Power Allocation Optimization

This paper proposes a matching scheme within non-orthogonal multiple access-integrated sensing and communication (NOMA-ISAC) based unmanned aerial vehicle (UAV) networks. NOMA-ISAC technology is utilized to optimize the downlink transmission delay (TD) and conditional mutual information (MI) performance for UAVs. The user clustering, flight trajectory and power allocation of UAVs are optimized jointly. Recognizing the varying population density of users, an improved density-weighted agglomerative hierarchical clustering (DWAHC) algorithm and a dynamic load balancing algorithm are applied to assign each user to the appropriate UAV. Then, a multi-agent deep reinforcement learning algorithm is employed to optimize the UAVstrajectory and transmission power through interactive learning. Experimental results demonstrate that the proposed scheme outperforms orthogonal multiple access (OMA) and other baseline algorithms in terms of reducing TD and improving conditional MI.