通信学报 ›› 2014, Vol. 35 ›› Issue (8): 33-39.doi: 10.3969/j.issn.1000-436x.2014.08.005

• 学术论文 • 上一篇    下一篇

基于观测矩阵优化的自适应压缩频谱感知

王韦刚1,2,杨震1,顾彬1,胡海峰1,3   

  1. 1 南京邮电大学 教育部宽带无线通信和传感网技术重点实验室,江苏 南京 210003
    2 南京邮电大学 电子科学与工程学院,江苏 南京 210003
    3 东南大学 移动通信国家重点实验室,江苏 南京 210008)
  • 出版日期:2014-08-25 发布日期:2017-06-29
  • 基金资助:
    国家重点基础研究发展计划( 973 计划)基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;南京邮电大学青蓝计划基金资助项目;东南大学移动通信国家重点实验室开放研究基金资助项目;江苏省博士后科研基金资助项目;中国博士后科学基金面上基金资助项目

Adaptive compressed spectrum sensing based on optimized measurement matrix

Wei-gang WANG1,2,Zhen YANG1,Bin GU1,Hai-feng HU1,3   

  1. 1 Key Laboratory of Wideband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    2 College of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    3 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210008, China
  • Online:2014-08-25 Published:2017-06-29
  • Supported by:
    The National Basic Research Program of China(973 Program);The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;Nanjing University of Posts and Telecommunications Blue Plan;Southeast University State Key Laboratory of Mobile Communications Research Fund;The Post-Doctoral Research of Jiangsu Province Funding Schemes;The Post-Doctoral Science Foundation of China

摘要:

推导了自适应压缩感知中的重构估算误差,研究了如何降低观测矩阵列向量之间的自相关性,分析了观测矩阵优化对压缩感知重构算法的影响。将观测矩阵优化与压缩感知自适应过程相结合,提出了基于观测矩阵优化的自适应压缩频谱感知算法。仿真结果证实,所提算法比传统算法重构时产生的均方误差(MSE)更低,在同一观测次数下检测概率更高,在达到同等接收操作性能(ROC)时所需观测次数更少。

关键词: 认知无线电, 压缩感知, 频谱检测, 观测矩阵

Abstract:

The estimation error of reconstruction by adaptive compressed sensing was derived, and the column vector autocorrelation of the observation matrix was reduced, and the impact of optimization process on compressed sensing re-construction algorithm was analyzed. Combining the observation matrix optimization and adaptive process, the spectrum sensing algorithm of optimized adaptive compression based on observation matrix was proposed. The simulation results show that the mean square error (MSE) of proposed algorithm is lower than traditional algorithm, and the probability of detection of proposed algorithm is higher on the same number of observations, and the required number of observations is fewer when achieving the same receiver operating performance (ROC).

Key words: cognitive radio, compressed sensing, spectrum detection, measurement matrix

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