Graph-Enhanced Critic Learning for Cooperative Spectrum Access in Multi-UAV Cognitive Radio Networks

Abstract

Cooperative spectrum access in multi-UAV cognitive radio networks requires decentralized control under dynamic primary-user activity, interference coupling, and partial observability. Standard multi-agent deep deterministic policy gradient (MADDPG) follows centralized training with decentralized execution, but its centralized critic usually processes the joint state-action context as a flat vector and therefore does not explicitly encode inter-UAV topology. This paper proposes a graph-enhanced centralized critic that injects topology-aware relational embeddings into value estimation while leaving decentralized actors unchanged. Under a unified eight-seed dynamic-spectrum protocol, the weighted radial basis function (weighted-RBF) graph critic improves mean reward from 4238.63 to 4346.55 and reduces collision rate from 0.1004 to 0.0383 relative to vanilla MADDPG. A stronger TD3-style multi-agent baseline (MATD3) reaches 4348.47 reward and 0.0495 collision rate, while MATD3 with the weighted-RBF graph critic further improves reward to 4379.29 and lowers collision rate to 0.0346. Additional topology ablation, high-interference, complexity, and scalability analyses show that graph-enhanced critic learning is most useful for collision control and interference-heavy operation, with centralized-training overhead and dense 16-secondary-user settings as practical limitations.