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Spectral gap filling in cognitive networks: A cooperative game-theoretic approach

机译:认知网络中的光谱缺口填充:一种合作博弈论方法

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An optimal joint channel selection and power control scheme is investigated in a cognitive network context, where the cognitive network is composed by multiple cognitive interference channels. Here, we take the fairness among multiple secondary users (SUs) and Pareto optimality measured by the capacity maximization into consideration. The complex cooperation and competition relationship among multiple SUs and primary users (PUs) is described with the refined signal-to-interference plus noise (SINR) definition. According to the Nash axioms from the Nash bargaining cooperative game, the newly built utility function is formulated, and the spectral gap-filling problem is formulated as cognitive capacity Nash product maximization (CCNPM). To improve the centralized algorithm design in in the cooperative game theory framework, we employ the dual decomposition technique to achieve the distributed bargaining approaches. The proposed approaches are with low implementation complexities and the little information exchange.
机译:在认知网络环境中研究了最优的联合信道选择和功率控制方案,其中认知网络由多个认知干扰信道组成。在这里,我们考虑了多个二级用户(SU)之间的公平性和通过容量最大化衡量的帕累托最优性。用精确的信噪比加噪声(SINR)定义描述了多个SU和主要用户(PU)之间复杂的协作和竞争关系。根据纳什议价合作博弈中的纳什公理,公式化了新建立的效用函数,并将频谱缺口填充问题公式化为认知能力纳什乘积最大化(CCNPM)。为了改进合作博弈理论框架中的集中式算法设计,我们采用对偶分解技术来实现分布式讨价还价的方法。所提出的方法具有较低的实现复杂度和很少的信息交换。

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