Interference-Aware Spectrum Resource Management in Dynamic Environment: Strategic Learning With Higher-Order Statistic Optimization

Abstract

Wireless spectrum environment is complex and uncertain in practical scenarios. For spectrum resource management, there are two crucial challenges to be resolved. First, channel states are always time-varying, thus violent fluctuations of interference links bring difficulties to interference mitigation. Second, the optimization efficiency degrades when jointly considering frequency coordination and power adaption. In this paper, we focus on the interference-aware resource management in a dynamic radio environment. First, we propose a new metric named effective weighted aggregate interference to capture the fluctuations of time-varying interference links. Then, we investigate the joint channel selection and power assignment problem from the perspective of game theoretic, and propose a novel mechanism called action freezing to significantly improve the optimization efficiency. Furthermore, we develop a higher-order statistic optimization based multi-agent strategic learning (HSOMSL) algorithm to alleviate the effect of fluctuating payoffs and obtain stable solutions. Simulation results illustrate that the proposed algorithm has lower experienced interference, higher achievable throughput, and better energy efficiency compared to existing state-of-the-art algorithms. In addition, it is verified to be effective in mobile scenarios.