Multi-UAV Joint Observation, Communication, and Policy in MEC

The use of multi-agent reinforcement learning methods (MARL) in mobile edge computing (MEC) environments enables multiple unmanned aerial vehicles (multi-UAV) to intelligently provide relay or computational offloading services to mission targets. UAV's observation range and communication methods between UAVs have a significant impact on multi-UAV collaboration strategy. For this purpose, we study the multi-UAV observation range dynamic control method and the optimal inter-UAV communication method. Our approach is to design a multi-UAV joint observation, communication, policy, and service collaboration protocol and study the optimization method of the protocol. We propose an expert-guided deep reinforcement learning framework to optimize this protocol. Each UAV's optimal radar observation range and inter-UAV communication method are learned using an information entropy value decomposition method. Through our observation and communication method, multi-UAV are able to obtain the most valuable information. Experiments demonstrate that our method can improve MEC's service coverage by 9.38%-21.88% compared to the classical MARL algorithm. Our method improves the radar observation efficiency and communication efficiency by 3.05%-38.9% and 8.55%-22.03%, respectively. The results show that this method improves multi-UAV energy utilization.