恶意干扰下基于斯坦伯格博弈的无人机动态频谱分配

针对恶意干扰场景下无人机群动态频谱分配问题,构建了基于斯坦伯格博弈的动态频谱分配模型,干扰机为斯坦伯格博弈的领导者,无人机群为斯坦伯格博弈的跟随者,设计了不同博弈参与者的效益函数,并证明了该博弈存在稳定的斯坦伯格均衡解。在此基础上设计了一种分层动态频谱分配算法,针对领导者采用Q学习选择干扰信道的场景下,跟随者采用随机学习自动机来确定信道分配策略。仿真结果表明,所提算法能够得到无人机用户的最优信道分配策略,有效提升无人机用户的总吞吐量性能,实现效益最大化。

Dynamic spectrum allocation algorithm of UAV based on Stackelberg game in the condition of malicious interference

Aiming at the problem of dynamic spectrum allocation of unmanned aerial vehicle (UAV) group in malicious interference scenario, a dynamic spectrum allocation model based on Stackelberg game was constructed. The jammer was the leader of Stackelberg game and the UAV group was the follower of Stackelberg game. The benefit functions of different game participants were designed. It was proved that the game had a stable Stackelberg equilibrium solution. On the basis of this, a hierarchical dynamic spectrum allocation algorithm was designed. When the leader used Q-learning to select the interference channel, the follower used random learning automata (SLA) to determine the channel allocation strategy. Simulation results showed that the proposed algorithm could obtain the optimal channel allocation strategy of UAV users, effectively improved the throughput performance of UAV users and maximized benefits.