任意形状的底层认知网络中聚合干扰的表征

Jing Guo, S. Durrani, Xiangyun Zhou
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引用次数: 9

摘要

本文描述了底层认知网络中由M个辅助用户(SU)引起的主用户(PU)的聚合干扰,其中采用适当的SU活动协议来限制由SU产生的干扰。与以往的工作不同,我们假设PU可以位于任意形状的凸网络区域内的任何位置。利用随机SU干扰的矩生成函数(MGF),导出了警戒区和多阈值SU活动协议的第n阶矩和第n阶累积量的一般表达式。利用累积量,研究了聚集干涉分布收敛到高斯分布的问题。此外,我们比较了文献中众所周知的封闭形式分布来近似聚集干涉的互补累积分布函数(CCDF)。我们的结果表明,在将总干涉近似为高斯分布时必须小心,即使对于大量的苏,因为收敛一般不是单调的。此外,对于任意形状的网络区域,移位对数正态分布提供了总体上最好的CCDF近似,特别是在分布尾部区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization of aggregate interference in arbitrarily-shaped underlay cognitive networks
This paper characterizes the aggregate interference at the primary user (PU) due to M secondary users (SUs) in an underlay cognitive network, where appropriate SU activity protocols are employed in order to limit the interference generated by the SUs. Different from prior works, we assume that the PU can be located anywhere inside an arbitrarily-shaped convex network region. Using the moment generating function (MGF) of the interference from a random SU, we derive general expressions for the n-th moment and the n-th cumulant of the aggregate interference for guard zone and multiple-threshold SU activity protocols. Using the cumulants, we study the convergence of the distribution of the aggregate interference to a Gaussian distribution. In addition, we compare the well-known closed-form distributions in the literature to approximate the complementary cumulative distribution function (CCDF) of the aggregate interference. Our results show that care must be undertaken in approximating the aggregate interference as a Gaussian distribution, even for a large number of SUs, since the convergence is not monotonie in general. In addition, the shifted lognormal distribution provides the overall best CCDF approximation, especially in the distribution tail region, for arbitrarily-shaped network regions.
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