基于权值分簇的多门限联合频谱检测算法

doi:10.19678/j.issn.1000-3428.0047129

计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 29-33. doi: 10.19678/j.issn.1000-3428.0047129

基于权值分簇的多门限联合频谱检测算法

彭军伟^1,2,韩志韧²,彭开志²

1.哈尔滨工程大学信息与通信工程学院,哈尔滨 150001; 2.武汉船舶通信研究所,武汉 430205

收稿日期:2017-05-10 出版日期:2018-06-15 发布日期:2018-06-15
作者简介:彭军伟(1987—),男,博士研究生,主研方向为认知无线电网络;韩志韧,研究员、博士生导师;彭开志,高级工程师、博士。
基金资助:
国家部委基金。

Multi-threshold Cooperative Spectrum Detection Algorithm Based on Weight Clustering

PENG Junwei ^1,2,HAN Zhiren²,PENG Kaizhi ²

1.College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China; 2.Wuhan Maritime Communication Research Institute,Wuhan 430205,China

Received:2017-05-10 Online:2018-06-15 Published:2018-06-15

摘要/Abstract

摘要：

联合频谱检测算法在检测授权用户频谱时,认知用户数量的增加会使有限的控制信道带宽成为制约算法实际应用的瓶颈。针对该问题,提出一种多门限联合频谱检测算法。在建立频谱检测模型的基础上,根据应用偏重要求的不同对认知用户进行权值分簇,从而避免控制信道阻塞。理论分析和Matlab仿真结果表明,在瑞利信道下,该算法可有效减小网络时延,提高系统检测概率。

关键词: 认知无线电网络, 频谱检测, 能量检测, 权值分簇, 多门限能量检测

Abstract:

Cooperative spectrum sensing algorithm requires a control channel when detecting the spectrum of primary user.However,the limitation of control channel bandwidth is the bottleneck of application of cooperative spectrum sensing when the number of cognitive users becomes very large.In order to solve this problem,this paper proposes a multi-threshold cooperative spectrum detection algorithm.Based on the establishment of a spectrum detection model,cognitive users are clustered with different application weight requirements to avoid congestion in the control channel.Results of theoretical analysis and simulation on the Matlab platform show that,the proposed algorithm can reduce the network delay and improve the detection probability of the system in Rayleigh channels.

Key words: Cognitive Radio Network(CRN), spectrum detection, energy detection, weight clustering, multi-threshold energy detection

中图分类号:

TN929.5

彭军伟,韩志韧,彭开志. 基于权值分簇的多门限联合频谱检测算法[J]. 计算机工程, 2018, 44(6): 29-33.

PENG Junwei,HAN Zhiren,PENG Kaizhi. Multi-threshold Cooperative Spectrum Detection Algorithm Based on Weight Clustering[J]. Computer Engineering, 2018, 44(6): 29-33.

https://www.ecice06.com/CN/Y2018/V44/I6/29

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