Safe causal-graph primal-dual multi-agent scheduling for energy- and latency-constrained edge-assisted cognitive radio networks

Edge-assisted cognitive radio networks require efficient scheduling mechanisms to jointly manage opportunistic spectrum access, task offloading, energy consumption, and latency constraints. Existing multi-agent scheduling approaches often rely on fixed penalty terms or average queue-based constraints, which may not effectively control service-level violations under uncertain spectrum availability and dynamic edge-resource contention. This work proposes SCOPE, a Safe Causal-Graph Primal-Dual multi-agent scheduling framework for energy- and latency-constrained edge-assisted cognitive radio networks. The major strength of SCOPE is its integrated design, where belief-state augmentation improves decision-making under imperfect spectrum sensing, dual-relational causal graph coordination separately models' interference coupling and computation-resource contention, and CVaR-based primal-dual optimization regulates tail-risk violations of latency and energy constraints. The framework follows a centralized-training and decentralized-execution structure, enabling coordinated learning during training while supporting scalable decentralized scheduling during deployment. Simulation results under dynamic user mobility, stochastic task arrivals, and varying primary-user activity show that SCOPE improves latency, energy efficiency, service-level constraint satisfaction, and throughput compared with existing scheduling methods. Ablation analysis further confirms the individual contribution of belief modeling, graph coordination, and risk-sensitive constraint enforcement.