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MAC-layer interference mitigation in dynamic and distributed environment: dynamic graphic game with stochastic learning

机译:动态和分布式环境中的MAC层干扰缓解:具有随机学习的动态图形游戏

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In this paper, we investigate the problem of channel selection for MAC-layer interference mitigation in the dynamic and distributed environment, in which each user only has its local information, and each user is dynamically active due to its specific data service requirement. Accordingly, a dynamic interference graph is defined to capture the dynamic and local interaction. Based on the defined interference graph, the MAClayer interference mitigation problem is formulated as a dynamic graphic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Furthermore, we design a fully distributed, online adaptive, stochastic learning algorithm for the interference-mitigation channel selection, which converges to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for MAC-layer interference mitigation in the dynamic and distributed environment.
机译:在本文中,我们研究了在动态和分布式环境中缓解MAC层干扰的信道选择问题,在该环境中,每个用户只有其本地信息,并且每个用户由于其特定的数据服务要求而处于动态活动状态。因此,动态干扰图被定义为捕获动态和局部相互作用。根据定义的干扰图,将MAClayer干扰缓解问题表述为动态图形游戏。事实证明,这是一个精确的潜在博弈,其中保证了纳什均衡(NE)的存在。此外,我们为干扰缓解信道选择设计了一种完全分布式的在线自适应随机学习算法,该算法收敛于公式化游戏的NE。最后,我们进行仿真以验证所提出的算法在动态和分布式环境中缓解MAC层干扰的有效性。

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