matlab data used for regenerating figures 7,8,9,10,11,12 in the manuscript

ABSTRACT This paper provides a study scenario of mobility control for the unmanned aerial vehicles that mount base stations, which are called UAVBSs. Mobility control of UAVBSs is considered a solution to serious problems such as interference and physical collisions between these UAVBSs. Thus, we formulated the problem of UAVBSs mobility control as an exact potential game, and we proposed three algorithms in this work to find the solution for that problem. The first algorithm is called the Neighbor Responsive Adaptive-Partial Synchronous Learning (NRA-PSL) algorithm, which works on controlling the UAVBS’s trajectory via conditioned response to its neighbor UAVBSs to select the action that guides the UAVBS to better direction. The second algorithm is UAVBSs-Better Direction Control (UAVBSs-BDC), which works iteratively with linear time to directly optimize the action selection based on the UAVBS’s utility. The third algorithm is called Utility-Driven Partial Synchronous Learning (UDPSL), which is inspired by the concept of the Partial Synchronous Binary Log Linear Learning algorithm to enhance and control the UAVBS’s trajectory. The simulation results showed the outperformance of the NRA-PSL algorithm by obtaining the global maxima and eliminating the interference among the UAVBSs. Whereas, the UAVBSs-BDC algorithm has proven its effectiveness in saving power and consuming less time-computation compared to other algorithms. Regarding the UDPSL algorithm, it has proved its advantage at low UAVBS’s velocity by producing high network capacity and interference mitigation.