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arXiv 1012.1095cs.NI
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认知无线电网络中主用户分离的二元推理

Binary Inference for Primary User Separation in Cognitive Radio Networks

Huy Nguyen, Guanbo Zheng, Zhu Han, Rong Zheng

摘要:

频谱感知是近年来认知无线电(CR)网络研究的热点,次要用户(SU)不断地监视信道条件以检测主要用户(PU)的存在。在本文中,我们超越频谱感知,并介绍了PU分离问题,这涉及到的问题,区分和表征PU的背景下,协同频谱感知和监测选择。监视器的观测值被建模为PU的底层二进制源的布尔或混合。我们首先证明使用的二进制或混合模型,而不是传统的线性混合模型,通过模拟研究。然后,我们设计了一种新的二进制推理算法的PU分离。不仅揭示了PU-SU关系,而且还可以推断PU在每个时隙的传输统计和活动。仿真结果表明,在没有任何先验知识的PU的活动,该算法实现了高的推理精度,即使在存在噪声的测量。

英文摘要:

Spectrum sensing receives much attention recently in the cognitive radio (CR) network research, i.e., secondary users (SUs) constantly monitor channel condition to detect the presence of the primary users (PUs). In this paper, we go beyond spectrum sensing and introduce the PU separation problem, which concerns with the issues of distinguishing and characterizing PUs in the context of collaborative spectrum sensing and monitor selection. The observations of monitors are modeled as boolean OR mixtures of underlying binary sources for PUs. We first justify the use of the binary OR mixture model as opposed to the traditional linear mixture model through simulation studies. Then we devise a novel binary inference algorithm for PU separation. Not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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