SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks

The author proposes a novel multi-agent reinforcement learning framework, CARLTON, to optimize channel allocation in cognitive interference networks. By utilizing a deep reinforcement learning approach, the algorithm demonstrates exceptional performance and robust generalization compared to existing methods.