Clustering Strategy of UAV Network Based on Deep Q-learning

In recent years, with the development and wide application of unmanned aerial vehicle (UAV) technology, the key technology of unmanned aerial vehicle network has become a new research focus. Compared with traditional networks, UAV networks have the characteristics of dynamic topology and limited energy. These characteristics have brought new challenges to the design and implementation of UAV communication systems. We need to redesign the organizational structure of the UAV network to reduce network power consumption, extend network operation time, and improve network stability. In this paper, we have studied the clustering of UAV networks and designed a clustering strategy. First, calculate the best number of clusters in the current network based on bandwidth balance, then use the K-means algorithm to quickly cluster the entire network, and finally implement a cluster head selection algorithm based on Deep Q-learning (DQN) in each cluster . We simulated the clustering strategy and analyzed the average link retention time, average cluster head retention time, network energy consumption, etc., which proved the efficiency and correctness of the algorithm proposed in this paper.