Research Article
Variable Dimension Measurement Matrix Construction for Compressive Sampling via m Sequence
@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
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.
