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Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check matrix

IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 83-91页

作者： Xiaoxia Song Yong Li Department of Computer Science ShanxiDatong University

A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

关键词： Data gathering regular low density parity check(RLDPC) matrix sensing matrix signal reconstruction wireless sensor networks(WSNs)

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COMPRESSED SPEECH SIGNAL sensing BASED ON THE STRUCTURED BLOCK SPARSITY WITH PARTIAL KNOWLEDGE OF SUPPORT

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Journal of Electronics(China) 2012年第1期29卷 62-71页

作者： JiYunyun YangZhen XuQian CollegeofCommunicationandInformationEngineering NanjingUniversityofPostsandTelecommunicationsNanjing210003Chin∞ KeyLabo{BroadbandWirelessCommunicationandSensorNetworkTechnology MinistryofEducationNanjingUniversityofPosts＆TelecommunicationsNanjing210003China

Structural and statistical characteristics of signals can improve the performance of Compressed sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the mixed program which is an improved version of the mixed program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.

关键词： Compressed sensing (CS) Speech signals sensing matrix Block sparsity

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A CLASS OF DETERMINISTIC CONSTRUCTION OF BINARY COMPRESSED sensing MATRICES

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Journal of Electronics(China) 2012年第6期29卷 493-500页

作者： Li Dandan Liu Xinji Xia Shutao Jiang Yong Graduate School at Shenzhen Tsinghua University Department of Computer Science and Technology Tsinghua University

Compressed sensing (CS) is an emerging technology in the field of signal processing, which can recover a sparse signal by taking very few samples and solving a linear programming problem. In this paper, we study the application of Low-Density Parity-Check (LDPC) Codes in CS. Firstly, we find a sufficient condition for a binary matrix to satisfy the Restricted Isometric Property (RIP). Then, by employing the LDPC codes based on Berlekamp-Justesen (B-J) codes, we construct two classes of binary structured matrices and show that these matrices satisfy RIP. Thus, the proposed matrices could be used as sensing matrices for CS. Finally, simulation results show that the performance of the proposed matrices can be comparable with the widely used random sensing matrices.

关键词： Compressed sensing (CS) Low-Density Parity-Check (LDPC) Codes Restricted Isowmetric Property (RIP) sensing matrix Deterministic construction

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COMPRESSED SPEECH SIGNAL sensing BASED ON THE STRUCTURED BLOCK SPARSITY WITH PARTIAL KNOWLEDGE OF SUPPORT

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Journal of Electronics(China) 2012年第Z1期 62-71页

作者： Ji Yunyun* ** Yang Zhen** Xu Qian* *(College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) **(Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts & Telecommunications, Nanjing 210003, China)

关键词： Compressed sensing (CS) Speech signals sensing matrix Block sparsity

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