认知网络频谱感知中异常值的估计

Awais Salman Qazi, S. Mahmood, A. U. Rehman, Waqas Ahmad
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引用次数: 0

摘要

这一主题的选择受到这样一个概念的影响,即有必要对代表认知网络中无线电通信中某些行为端点的不同属性进行统计分析,以研究这些参数中出现的异常值。本文的文献综述部分详细说明了认知无线电的重要性。本报告的目的是概述认知无线电网络中出现的模式,并通过学习在估计期间显示异常值的哪种属性来寻求对数据的理解。在本研究过程中,我们发现对异常值的研究需要对数据进行预处理,在此过程中对数据的某些异常进行研究,然后去除,从而优化数据集。在此过程中,出现了两个主要属性SNR和Lambda,并在统计上显示出有助于估计异常值的模式。关键词:信噪比,Lambda, Outliers, PU, SU, cr。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Estimation of outliers in cognitive networks spectrum sensing
The choice of this topic was influenced from the concept that statistical analysis of different attributes representing certain endpoints of behavior during radio communication in cognitive networks was necessary to study the outliers occurring in those parameters. The importance of cognitive radio is explained in detail in the literature review section of this paper. The purpose of this report is to do an overview of emerging patterns in cognitive radio networks and seek an understanding of data by learning what kind of attributes that display outliers during estimation. During the course of this research, it has come to light that study of outliers require preprocessing of data during which certain anomalies of data are studied and then removed thus optimizing the dataset. In the process, two major attributes SNR and Lambda have emerged and statistically shown a pattern that helped with the estimation of outliers. Key words: SNR, Lambda, Outliers, PU, SU, CRs.
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