Game-Theoretic Optimization of Multiple Interfering Base Stations Deployment

The use of malicious uncrewed aerial vehicle (UAV) poses a threat to the security of sensitive airspace such as airports and military bases, and this potential threat requires interference devices to neutralize and weaken it. This paper investigates the optimization of navigation and communication interference base stations (IBSs) deployment for UAV approaching sensitive target by adjusting IBSs positions to maximize interference along the UAV's optimal flight path, while simultaneously minimizing interference with the supportive devices of the interference system. Specifically, this article formulates the IBSs deployment optimization as a game-theoretic mathematical framework to determine the optimal deployment strategy. First, an objective function is defined to assess the communication and navigation performance of both the UAV and supportive devices, incorporating a probabilistic channel attenuation model. This function aims to maximize interference on the UAV while minimizing impact on supportive equipment by optimizing the IBSs deployment strategy. Second, within the framework of the optimization problem, the IBSs deployment problem is demonstrated to constitute an exact potential game, thereby ensuring the existence of a pure-strategy Nash equilibrium (NE). To further validate that the IBSs deployment strategy derived from the optimal UAV trajectory - characterized as the path with minimal interference - is indeed equilibrium, we also develop a game-theoretic framework that integrates both IBSs deployment and UAV path planning strategies. The NE strategy derived from this framework establishes a robust theoretical foundation for the subsequent optimization algorithm of IBSs deployment and UAV flight path determination. Finally, in response to the complexity and highly dynamic nature of the deployment problem, we specifically propose an algorithm architecture for solving NE strategies based on maximum entropy reinforcement learning (MERL) techniques. Simulation and comparision experiments have demonstrated that the algorithm can achieve maximum interference to UAV and minimum interference to supportive devices by optimizing IBSs deployment, showcasing advantages in balancing internal and external interference.