Collective Intelligence-Driven UAV Placement and Resource Allocation in Cell-Free MIMO Networks

Multi-agent systems achieve distributed intelligence through collaborative cooperation, enhancing decision-making efficiency and system robustness in complex environments. As a powerful tool for modeling competitive and cooperative interactions among agents, game theory has attracted increasing attention for its capability in resource allocation and performance optimization. This paper investigates a cell-free multiple-input multiple-output (MIMO) communication system that serves unmanned aerial vehicle (UAV) users, focusing on the joint optimization of UAV positioning and access point (AP) power allocation. To accurately model the interactions among agents, a hierarchical Stackelberg game is formulated for the downlink rate maximization problem. It is proven that both the leader and follower subgames are exact potential games, and the existence of the subgame’s Nash equilibrium (NE) is proven based on the properties of exact potential games (EPG), which also ensures the existence of the Stackelberg equilibrium (SE). Furthermore, a hierarchical iterative algorithm is proposed to achieve the SE. Simulation results demonstrate that the proposed algorithm effectively enhances the system’s downlink transmission rate.