Joint Computation Offloading, Role, and Location Selection in Hierarchical Multicoalition UAV MEC Networks: A Stackelberg Game Learning Approach

Qihui Wu, Jiaxin Chen, Yuhua Xu, Nan Qi, Tao Fang, Youming Sun, Luliang Jia

Abstract: Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.