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Collaborative spectrum sensing in multi-channel cognitive networks: A coalition game approach

机译:多通道认知网络中的协作频谱感知:联盟博弈方法

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Collaborative spectrum sensing among secondary users (SUs) in cognitive radio (CR) networks is shown to yield a significant performance improvement. However, much previous work in CR networks focus on single channel sensing performed by SU cooperatively while spectrum sensing on multiple channels is largely ignored. In order to overcome the foregoing shortcoming, coalition game theory is introduced. In this paper, we mainly study the throughput maximization problem which considers optimizing the network throughput and channel sensing period synchronously. Moreover, the the missing detection and false alarm probability are also constrained. Then a modified merge and split algorithm (MMSA) is designed. Extensive simulation results illustrate that the proposed MMSA algorithm can significantly improve the overall network throughput and guarantee low missing detection and false alarm probability for channels.
机译:认知无线电(CR)网络中的二级用户(SU)之间的协作频谱感知已显示出显着的性能提升。但是,CR网络中许多以前的工作都集中在SU协同执行的单通道感测上,而在多通道上的频谱感测却被忽略。为了克服上述缺点,引入了联盟博弈理论。在本文中,我们主要研究吞吐量最大化问题,该问题考虑了同步优化网络吞吐量和信道感知周期。此外,漏检和误报概率也受到限制。然后设计了一种改进的合并和拆分算法(MMSA)。大量的仿真结果表明,所提出的MMSA算法可以显着提高整体网络吞吐量,并确保较低的漏检率和信道误报率。

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