Abstract: Based on unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC), the flexible construction of line-of-sight links significantly improves the communication quality of a system and plays an important role in handling computationally intensive and latency-sensitive tasks. However, single UAV-assisted MEC suffers from a limited coverage range and long task processing time. This study considers multiple UAVs with MEC servers to provide offloading computing services for multiple ground users, and it presents the problem of maximizing the minimum number of computation bits. Under the constraints of limited energy consumption and no-fly zones, this study jointly optimized user scheduling, user upload power, task offloading time, local computation time, and UAV trajectory. An iterative optimization algorithm based on block coordinate descent was introduced to provide users with a fairer computation offloading service. The original problem is divided into four subproblems, and the non-convex subproblem is transformed into a convex optimization subproblem via successive convex approximations. The simulation results show that compared with other benchmark schemes, the proposed joint optimization scheme can significantly increase the number of maximum-minimum computation bits.

Key words: mobile edge computing (MEC), multiple unmanned aerial vehicles, block coordinate descent method