Computation Bits Maximization in UAV-Enabled Mobile-Edge Computing System

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.