北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2014, Vol. 37 ›› Issue (6): 115-119.doi: 10.13190/j.jbupt.2014.06.024

• 研究报告 • 上一篇    下一篇

基于EEF准则的认知无线电宽带频谱感知

申滨, 喻俊, 黄琼, 陈前斌   

  1. 重庆邮电大学 移动通信技术重点实验室, 重庆 400065
  • 收稿日期:2014-03-18 出版日期:2014-12-28 发布日期:2014-10-17
  • 作者简介:申滨(1978-),男,教授,E-mail:shenbin@cqupt.edu.cn.
  • 基金资助:

    国家自然科学基金项目(61201205,61379159); 重庆市自然科学基金项目( CSTC2012JJA40043); 重庆市博士后科研项目特别资助项目(Xm201308)

EEF Criterion Based Wideband Spectrum Sensing Used in Cognitive Radio

SHEN Bin, YU Jun, HUANG Qiong, CHEN Qian-bin   

  1. Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2014-03-18 Online:2014-12-28 Published:2014-10-17

摘要:

提出了基于指数嵌入族(EEF)准则的认知无线电宽带频谱感知算法. 在EEF准则宽带感知算法的基础上,充分考虑信号子空间和噪声子空间的性质,利用Gerschgorin酉变换改进EEF算法,提出了基于Gerschgorin EEF(GEEF)的宽带感知算法. 该方案主要是利用EEF准则或GEEF准则估算出授权频带中被主用户占用子带集合的势,从而完成宽带授权频段上空闲频带的感知,以便于被次级用户动态接入. 理论推导和仿真结果表明,所提出的方案无须知晓任何有关噪声功率和主用户信号的先验信息,对噪声功率的不确定性具有鲁棒性.

关键词: 认知无线电, 宽带频谱感知, 指数嵌入族准则, Gerschgorin酉变换

Abstract:

Exponentially embedded families (EEF) criterion based wideband spectrum sensing schemes were proposed for cognitive radios. Based on the EEF criterion, the signal and noise subspaces were considered in spectrum observation data and the EEF wideband sensing was improved by employing Gerschgorin unitary transformation in data processing. Correspondingly, the developed wideband sensing algorithm was operated under Gerschgorin EEF (GEEF) criterion. The proposed two algorithms estimate the cardinality of the primary user signal occupied subband set via EEF or GEEF criterion to identify idle subbands for secondary users dynamically accessing the licensed frequency band. Analysis and simulation verify that the proposed algorithms do not need any a prior knowledge of the noise power and the primary user signal. They are robust against the noise power uncertainty problem.

Key words: cognitive radio, wideband spectrum sensing, exponentially embedded families criterion, Gerschgorin unitary transformation

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