Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Variable Dimension Measurement Matrix Construction for Compressive Sampling via m Sequence

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_22,
        author={Jingting Xiao and Ruoyu Zhang and Honglin Zhao},
        title={Variable Dimension Measurement Matrix Construction for Compressive Sampling via m Sequence},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Measurement matrix Compressed sensing Modulated wideband converters M sequence optimum pairs},
        doi={10.1007/978-3-319-73564-1_22}
    }
    
  • Jingting Xiao
    Ruoyu Zhang
    Honglin Zhao
    Year: 2018
    Variable Dimension Measurement Matrix Construction for Compressive Sampling via m Sequence
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_22
Jingting Xiao1,*, Ruoyu Zhang1,*, Honglin Zhao1,*
  • 1: Communication Research Center, Harbin Institute of Technology
*Contact email: hitxjting@163.com, hitzhangruoyu@163.com, hlzhao@hit.edu.cn

Abstract

Signal acquisition in ultra-high frequency is a challenging problem due to high cost of analog-digital converter. While compressed sensing (CS) provides an alternative way to sample signal with low sampling rate, the construction of measurement matrix is still challenging due to hardware complexity and random generation. To address this challenge, a variable dimension deterministic measurement matrix construction method is proposed in this paper based on cross-correlation characteristics of m sequences. Specifically, a lower bound of the spark of measurement matrix is derived theoretically. The proposed measurement matrix construction method is applicable to compressive sampling system to improve the quality of signal reconstruction, especially for modulated wideband converter (MWC) architecture. Simulation results demonstrate that the proposed measurement matrix is superior to random Gauss matrix and random Bernoulli matrix.