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Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery

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Wireless and Satellite Systems (WiSATS 2019)

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Abstract

Clustered sparse signals recovery with unknown cluster sizes and locations is considered in this paper. An improved alternative extended block sparse Bayesian learning algorithm (AEBSBL) is proposed. The new algorithm is motivated by the graphic models of the extended block sparse Bayesian learning algorithm (EBSBL). By deriving the graphic model of EBSBL, an equivalent cluster structured prior for sparse coefficients is obtained, which encourages dependencies among neighboring coefficients. With the sparse prior, other necessary probabilistic modelings are constructed and Expectation and Maximization (EM) is applied to infer all the unknowns. The alternative algorithm reduces the unknowns of EBSBL. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. School Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lu Wang, Lifan Zhao & Guoan Bi

  2. School of information and communication, Dalian University of Technology, Dalian, China

    Xin Liu

Authors
  1. Lu Wang
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  2. Lifan Zhao
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  3. Guoan Bi
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  4. Xin Liu
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Corresponding author

Correspondence to Guoan Bi .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, L., Zhao, L., Bi, G., Liu, X. (2019). Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-19153-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19152-8

  • Online ISBN: 978-3-030-19153-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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