Distributed Joint Optimization of Deployment, Computation Offloading and Resource Allocation in Coalition-based UAV Swarms

Different from terrestrial networks where mobile edge computing (MEC) servers are fixed, UAVs can move around to change the channel quality between servers and terminals, and thus can further improve the network performance. Such utilization brings in new challenges for coalition-based UAV swarms since both intra- and inter-coalition offloading behaviors are coupled with locations. Therefore, this paper investigates the joint deployment, computation offloading, power control and channel access optimization problem in the coalition-based UAV swarms. To satisfy the distributed character of UAV swarms, a random best and better response (RAN-BBR) algorithm is proposed to solve the problem. The algorithm compares multiple strategies in each iteration which not only fastens the convergence speed but also avoids the cost of traversing all strategies. Simulation results verify the effectiveness of the proposed method which is much more energy saving than the methods without deployment optimization.