非同分布高斯信号背景下基于增强能量检测的协作感知

doi:10.13190/j.jbupt.2018-071

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (6): 39-44.doi: 10.13190/j.jbupt.2018-071

非同分布高斯信号背景下基于增强能量检测的协作感知

黄河, 袁超伟

北京邮电大学信息与通信工程学院, 北京 100876

收稿日期:2018-04-20 出版日期:2018-12-28 发布日期:2018-12-24
作者简介:黄河(1986-),男,博士生,E-mail:huangh@bupt.edu.cn;袁超伟(1960-),男,教授,博士生导师.
基金资助:
国家电网总部科技项目（SGLNDK00KJJS1700200）

Cooperative Spectrum Sensing with Enhanced Energy Detection under Non-Identical Gaussian Background Noise

HUANG He, YUAN Chao-wei

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-04-20 Online:2018-12-28 Published:2018-12-24

摘要/Abstract

摘要： 能量检测算法应用于无线通信场景中时存在信噪比（SNR）墙和噪声不确定性问题，对此，在认知无线电非同分布高斯信号背景下，提出了基于增强能量检测的协作感知算法.在低SNR条件下，给出了采用增强能量检测算法的次用户检测概率和虚警概率，并推导出达到最小总错误率的最优协作用户数表达式.理论分析和仿真实验结果表明，相对其他克服噪声不确定性的能量检测算法，所提出的算法具有更优的检测性能.

关键词: 认知无线电, 协作感知, 噪声不确定性

Abstract: In wireless communication scenarios, energy detection has disadvantage in signal-to-noise ratio (SNR) wall and noise uncertainty. The article presents enhanced energy detection with cooperative spectrum sensing under non-identical Gaussian background noise in cognitive radio. Furthermore, the probability of detection and the probability of false alarm have been derived for enhanced energy detection under low SNR condition in cooperative spectrum sensing, and the number of optimized collaboration users has been deduced for the minimum total error rate. Theoretical analysis and numerical results show that proposed schemes have better detection performance than other energy detection plans for solving noise uncertainty problem.

Key words: cognitive radio, cooperative sensing, noise uncertainty

中图分类号:

TN911.23

黄河, 袁超伟. 非同分布高斯信号背景下基于增强能量检测的协作感知[J]. 北京邮电大学学报, 2018, 41(6): 39-44.

HUANG He, YUAN Chao-wei. Cooperative Spectrum Sensing with Enhanced Energy Detection under Non-Identical Gaussian Background Noise[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(6): 39-44.

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非同分布高斯信号背景下基于增强能量检测的协作感知

Cooperative Spectrum Sensing with Enhanced Energy Detection under Non-Identical Gaussian Background Noise

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