Joint Channel Access and Power Control Optimization in Large-Scale UAV Networks: A Hierarchical Mean Field Game Approach

Abstract

With the development of the new generation communications, unmanned aerial vehicle base stations (UAV-BSs) have been widely used to enhance the terrestrial communications. Because of the increasing demand for communication, the scale of the networks is constantly expanding. This paper investigates a joint channel access and power control optimization in large-scale UAV coverage networks, and proposes a hierarchical mean field game approach. Firstly, considering the quality of service (QoS) requirement and energy consumption, the cost function of UAV-BSs is designed. A joint channel access and power control optimization framework is built to improve the efficiency of resource utilization by minimizing the cost function. Then, the joint optimization problem is formulated as a hierarchical game model whose exterior is a channel access game and interior is a multiple mean fields game (MMFG). The channel access game is proved to be a potential game and have at least one Nash Equilibrium (NE). As feedback for channel access optimization, power control policy is obtained by MMFG. A near-optimal algorithm for joint optimizations is designed to achieve the NE. Besides, aiming to the networks with limited computing capability, we also propose a fast optimization approach. The original optimization problem is divided into two independent sub-problems. The channel optimization sub-problem is formulated as a local altruistic game model, then the power policy sub-problem is solved by MMFG. A distributed algorithm based on spatial adaptive play and better response is proposed. Finally, the simulation results verify the validity of the proposed algorithms. The near-optimal optimization method has a better performance while the fast optimization method has a higher speed of the convergence. Compared with the traditional approaches, both of them improve resources utilization efficiency greatly.