Multiple UAVs Trajectory Optimization in Multicell Networks With Adjustable Overlapping Coverage

Trajectory optimization of unmanned aerial vehicles (UAVs) operating as flying base stations (FBSs) evolved as a novel integration component in beyond 5G (B5G) networks and has recently received significant research attention. Notably, the vast majority of previous research has mainly concentrated on the case of a single terrestrial macro base station (BS) which is used as a depot for multiple FBSs. In this article, we focus on the more general use case where multiple FBSs located at different macro-BSs used as a depot serve ground users (GUs) at cluster points (CPs). To this end, we formulate the FBSs trajectory optimization problem using a mixed-integer linear programming (MILP) formulation with the aim to minimize the total travel time (TTT) of the FBSs in a multicell network in which their cell coverage or boundary is adjustable for the FBSs deployment; creating in that sense virtual cells for the FBSs. Furthermore, heuristic algorithms are proposed to provide competitive solutions and reduce the computational time in view of the curse of dimensionality of the original problem. Numerical investigations reveal that the proposed FBSs path planning optimization solutions decrease the TTT and increase the efficiency of offloading collected data for the FBSs deployment with gains up to approximately 23% and 19%, respectively, compared to nominal schemes that consider the predefined coverage range of the cells or no cell boundaries. Aside from the above, compared to previously proposed nominal strategies, the proposed schemes achieve an almost 27% improvement in terms of fairness (Jain’s index) on the FBS traveling time.