Intelligent Task Offloading for Caching-Assisted UAV Networks

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks in unmanned aerial vehicle (UAV) networks, it is urgent to efficiently allocate limited network resources to improve network performance. In this paper, we propose an intelligent task offloading scheme to optimize resource allocation in UAV networks with content caching. Specifically, we formulate the joint optimization of task offloading and resource allocation as a latency minimization model for the caching-assisted UAV system. Then, a new deep reinforcement learning (DRL) algorithm is designed to make offloading and resource allocation decisions based on current network state information, significantly improving resource utilization. Numerical results indicate that the model significantly reduces network latency in comparison to its existing benchmarks in caching-assisted UAV networks.