Latency Minimization Resource Allocation and Trajectory Optimization for UAV-Assisted Cache-Computing Network With Energy Recharging

Air-ground integrated network is able to make up for the limitations of small coverage as well as fixed resource deployment of ground 5G network and provide flexible services via edge computing. However, duplicated data may be offloaded to edge server, resulting in waste of network resources. In the meantime, due to the limited on-board energy storage of unmanned aerial vehicle (UAV), the endurance and trajectory optimization of UAV need to be focused on. Moreover, the high dynamics of network nodes will lead to the dilemma of information uncertainty and curse of dimensionality. In order to tackle the above challenges, we design a UAV-assisted heterogeneous cache-computing network model, among which, devices are divided into two categories: the request ground device (RGD) which requires task offloading and the free ground device (FGD) which can process offloaded tasks from the RGD through the device-to-device (D2D) link. Then we formulate a problem of jointly optimizing cache strategy, task segmentation, computing resource allocation and UAV trajectory planning to minimize the system latency constrained by UAV energy recharging. Due to the coexistence of discrete and continuous variables as well as coupling between long-term energy constraint and short-term decision making, we decompose it into two sub-problems. Low complexity matching algorithm is used to select the optimal UAV task caching strategy, while Lyapunov optimization is leveraged to decompose the second sub-problem, for which CVX is utilized to solve the task segmentation and local computing resource allocation, and soft actor-critic (SAC) approach is used to realize the computing resource allocation of FGD and UAV plus trajectory planning. Extensive simulation results showcase the advantages of our algorithm in reducing the system latency compared to benchmarks.