卷积时序融合网络在无人机集群频谱优化中的应用OA

This study proposed a spectrum resource optimization algorithm for UAV swarms under dynamically changing communication tasks and interference environments,based on a convolutional temporal fusion network.Specifically,the study leveraged the local feature extraction capability of convolutional neural networks and the temporal modeling ability of long short-term memory networks to enhance the autonomous learning and adaptability of UAV swarms.By integrating double deep Q-network within a multi-agent framework,distributed online training was performed,enabling each UAV in the swarm to respond quickly to dynamic tasks and interference while optimizing spectrum resources based solely on local observations.Simulation results show that,in environments with dynamic communication tasks and interference,the proposed algorithm outperforms conventional methods,not only improving spectrum resource utilization efficiency but also demonstrating excellent stability.