Power Control of Switched Wireless Cellular Networks by Probability Transition Rates Optimization

Wireless cellular networks require efficient distributed power control to ensure that all users achieve the desired signal-to-interference ratio while minimizing transmission power. Existing algorithms demonstrate exponentially fast convergence under fixed network conditions. In this work, we extend this framework to networks with randomly switching topologies. We consider two scenarios: one where network parameters follow a random process, allowing parameter optimization for faster convergence; and another where parameters are fixed, and convergence is optimized by adjusting the switching rate. To address the NP-hardness of general control optimization, we propose a novel framework based on difference-of-convex (DC) programming, which efficiently yields globally optimal power allocations under random switching. Simulation results show that the proposed method satisfies SINR requirements and significantly accelerates convergence in various dynamic network settings.