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Sparse Multipath Channel Estimation Using Regularized Orthogonal Matching Pursuit Algorithm

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Electrical, Information Engineering and Mechatronics 2011

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 138))

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Abstract

Conventional linear channel estimation methods, such as the least squares, are unavailable in practical applications since all entries of the solution obtained by these methods were nonzeros. Using the sparsity of channel, several methods have been proposed, such as orthogonal matching pursuit (OMP) and convex program. However, OMP algorithm is unstable in the case of highly redundant dictionary and the convex program method is hard to implement due to its complexity. In this paper, a novel sparse channel estimate strategy named as regularized orthogonal matching pursuit algorithm is proposed. This algorithm combines the advantage of OMP algorithm and convex program methods. Numeral experiments demonstrate that the proposed algorithm is effective for the problem of sparse multi-path channel estimation.

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, China

    Rui Wang & Jing Lu

Authors
  1. Rui Wang
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  2. Jing Lu
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Corresponding author

Correspondence to Rui Wang .

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Editors and Affiliations

  1. , School of Electrical Engineering and, Henan Polytechnic University, Jiaozuo, China, People's Republic

    Xudong Wang

  2. , School of Electrical Engineering and, Henan Polytechnic University, Jiaozuo, China, People's Republic

    Fuzhong Wang

  3. , College of Elementary Education, Chongqing Normal University, Chongqing, China, People's Republic

    Shaobo Zhong

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Wang, R., Lu, J. (2012). Sparse Multipath Channel Estimation Using Regularized Orthogonal Matching Pursuit Algorithm. In: Wang, X., Wang, F., Zhong, S. (eds) Electrical, Information Engineering and Mechatronics 2011. Lecture Notes in Electrical Engineering, vol 138. Springer, London. https://doi.org/10.1007/978-1-4471-2467-2_16

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  • DOI: https://doi.org/10.1007/978-1-4471-2467-2_16

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2466-5

  • Online ISBN: 978-1-4471-2467-2

  • eBook Packages: EngineeringEngineering (R0)

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