面向复杂电磁干扰环境的 Greedy-PPO 智能频谱共享决策

Considering the challenge of continuous and discrete hybrid action coupling decision-making, an intelligent spectrum sharing technology based on reinforcement learning is studied to solve the problem of intense frequency conflict of multi-functional electromagnetic equipment in complex electromagnetic environment. Firstly, considering the influence of many factors such as the frequency rules of the own side and the jamming side, a sophisticated model of the complex electromagnetic interference environment is developed. Based on this, a spectrum sharing efficiency evaluation index for radar communication integrated equipment under multitask requirements is designed. Secondly, a Greedy Proximal Policy Optimization(Greedy-PPO)intelligent spectrum sharing decision algorithm is proposed, which decouples the discrete continuous action space and uses the PPO method to optimize the allocation of transmission power. Then, the Greedy method is employed to solve the problem of spectrum discrete optimization allocation and obtain an approximately optimal joint spectrum sharing strategy. Finally, through simulation experiments, it is verified that the Greedy PPO algorithm can improve the overall performance by 48% and 15% compared to greedy algorithms and DDQN algorithms, respectively, demonstrating excellent performance of spectrum utilization.