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.

中文翻译：

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动态环境中的干扰感知频谱资源管理：具有高阶统计优化的战略学习


实际场景中无线频谱环境复杂且不确定。对于频谱资源管理来说，有两个关键挑战需要解决。首先，信道状态总是时变的，干扰链路的剧烈波动给干扰抑制带来困难。其次，当同时考虑频率协调和功率自适应时，优化效率会降低。在本文中，我们重点关注动态无线电环境中的干扰感知资源管理。首先，我们提出了一种名为有效加权聚合干扰的新度量来捕获随时间变化的干扰链路的波动。然后，我们从博弈论的角度研究联合通道选择和功率分配问题，并提出一种称为动作冻结的新颖机制，以显着提高优化效率。此外，我们开发了一种基于高阶统计优化的多智能体策略学习（HSOMSL）算法，以减轻收益波动的影响并获得稳定的解决方案。仿真结果表明，与现有最先进的算法相比，所提出的算法具有更低的干扰、更高的可实现吞吐量和更好的能源效率。此外，还验证了其在移动场景下的有效性。