Binary sparse signal recovery with binary least squares

Abstract The problem of obtaining a š¾-sparse binary signal š± from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi mathvariant="bold">y</m:mi> <m:mo>=</m:mo> <m:mrow> <m:mrow> <m:mi mathvariant="bold">Φ</m:mi> <m:mo>⁢</m:mo> <m:mi mathvariant="bold">x</m:mi> </m:mrow> <m:mo>+</m:mo> <m:mi mathvariant="bold-italic">ν</m:mi> </m:mrow> </m:mrow> </m:math> \mathbf{y}=\boldsymbol{\Phi}\mathbf{x}+\boldsymbol{\nu} is often encountered in communications and signal processing. Based on orthogonal least squares (OLS), a greedy algorithm guaranteeing the support recovery of a š¾-sparse binary signal is presented in this paper. Using the mutual coherence and restricted isometric properties of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="bold">Φ</m:mi> </m:math> \boldsymbol{\Phi} , sufficient conditions ensuring the exact recovery of the support of š± are given. Finally, compared with binary matching pursuit (BMP) and OLS, the simulation experiments imply the advantage of the proposed algorithm.

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Publication Details

Journal
Journal of Inverse and Ill-Posed Problems
Published
2025-09-16
DOI
https://doi.org/10.1515/jiip-2023-0008
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
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article

Binary sparse signal recovery with binary least squares

Haifeng Li, Qi Chen
Journal of Inverse and Ill-Posed Problems
Sparse and Compressive Sensing Techniques
article

Binary sparse signal recovery with binary least squares

Haifeng Li, Qi Chen
article en

Abstract

Abstract The problem of obtaining a š¾-sparse binary signal š± from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi mathvariant="bold">y</m:mi> <m:mo>=</m:mo> <m:mrow> <m:mrow> <m:mi mathvariant="bold">Φ</m:mi> <m:mo>⁢</m:mo> <m:mi mathvariant="bold">x</m:mi> </m:mrow> <m:mo>+</m:mo> <m:mi mathvariant="bold-italic">ν</m:mi> </m:mrow> </m:mrow> </m:math> \mathbf{y}=\boldsymbol{\Phi}\mathbf{x}+\boldsymbol{\nu} is often encountered in communications and signal processing. Based on orthogonal least squares (OLS), a greedy algorithm guaranteeing the support recovery of a š¾-sparse binary signal is presented in this paper. Using the mutual coherence and restricted isometric properties of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi mathvariant="bold">Φ</m:mi> </m:math> \boldsymbol{\Phi} , sufficient conditions ensuring the exact recovery of the support of š± are given. Finally, compared with binary matching pursuit (BMP) and OLS, the simulation experiments imply the advantage of the proposed algorithm.

Journal of Inverse and Ill-Posed Problems
Henan Normal University (CN)
Openalex Percentile: Top 73%
Sparse and Compressive Sensing Techniques
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