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Interference Aware Routing Game for Cognitive Radio Multi-Hop Networks

机译:认知无线电多跳网络的干扰感知路由游戏

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In this paper, we introduce a distributed dynamic routing algorithm in multi-hop cognitive radio (CR) networks, in which secondary users (SUs) want to minimize their interference to the primary users (PUs) while keeping the delay along the route low. We employ a cognitive pilot channel (CPC) for SUs to be able to access the information about PUs, including PUs' locations and channel conditions. Medial axis with a relaxation factor is used as a reference path for the routing, along which we develop a hierarchical structure for multiple sources to reach their destinations. We introduce a temporal and spatial dynamic non-cooperative game to model the interactions among the SUs as well as their influences on the PUs, and obtain by backward induction a set of mixed (behavioral) Nash equilibrium strategies. We also employ a multi-stage fictitious play learning algorithm for distributed routing, which minimizes the overall interference from the SUs to the PUs, as well as the average packet delay along the route from the SU nodes to their destinations. Simulation results show that our proposed algorithm can avoid congestion in the CR network and minimize delay while keeping the interference level low.
机译:在本文中,我们介绍了一种在多跳认知无线电(CR)网络中的分布式动态路由算法,其中,次要用户(SU)希望最大程度地减少其对主要用户(PU)的干扰,同时保持沿路径的延迟较低。我们为SU使用认知导频信道(CPC),以便能够访问有关PU的信息,包括PU的位置和信道条件。具有松弛因子的中间轴用作路由的参考路径,沿着该路径我们为多个源建立了到达其目的地的层次结构。我们引入了一个时空动态非合作博弈模型来模拟SU之间的相互作用以及它们对PU的影响,并通过向后归纳得出一套混合的(行为的)纳什均衡策略。我们还为分布式路由采用了多阶段虚拟游戏学习算法,该算法将SU到PU的总体干扰以及从SU节点到其目的地的路由的平均数据包延迟降到最低。仿真结果表明,我们提出的算法可以避免CR网络中的拥塞,并最大程度地减少延迟,同时保持较低的干扰水平。

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