Latency Optimization for Multi-UAV-Assisted Task Offloading in Air-Ground Integrated Millimeter-Wave Networks

In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.