Joint resource optimization and trajectory design for energy minimization in UAV-assisted mobile-edge computing systems

This paper studies an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, where a rotary-wing UAV equipped with computing platforms is used to assist ground users (GUs) with insufficient computing resource. Each GU offloads part of the task to the UAV for computation through the uplink, and the remaining computation task is computed by itself. Then, UAV will return the results to the corresponding GU via downlink. Considering the results for each GU is not only used by itself, but may also be sent to other GUs for data fusion, we thus design two specific scenarios in this paper: (1) homologous UAV-assisted MEC scenario, i.e. UAV receives the computation task of GUs and then returns the results to the identical GUs, and (2) non-homologous UAV-assisted MEC scenario, i.e., UAV receives the computation task of source GUs and returns the results to the corresponding destination GUs. For these two scenarios, we propose an energy minimization problem by jointly optimizing task partition, the time slot length and UAV trajectory. In particular, we use the path discretization approach to convert the problem as a problem form which is finite variables. Since the problem is non-convex, we use successive convex approximation (SCA) techniques to tackle the non-convexity. Considering the initial trajectory has an important affect on the experimental result, then a specific trajectory initialization design via combining with the Pickup-and-Delivery Problem (PDP) is proposed. Numerical results proof our proposed design is superior compared with the benchmark cases.