LAUSR

Cache-enabled UAVs are widely used as edge servers to provide on-demand content services for ground users. Most of prior works deploy UAVs based on user locations and explore the benefits of UAV caching under static content library. Motivated by this observation, we further consider heterogeneous user activity level and dynamic content library. We formulate a joint optimization problem of UAV deployment and content placement to minimize the average request delay, which is proved to be NP-hard. To tackle this problem efficiently, we decompose this optimization problem into two sub-problems, UAV deployment problem and content placement problem. Specifically, we first jointly consider user activity level and user locations for UAV deployment using weighted K-means method. After the UAV deployment, we propose a Q-learning algorithm to learn the content placement directly, rather than estimating the content popularity first, which is not effective when content library is dynamically changing. The simulation results show that the proposed algorithm outperforms the benchmark algorithms when dynamic content library and heterogeneous user activity level are considered.