Sparse Multipath Channel Estimation Using Regularized Orthogonal Matching Pursuit Algorithm

  • Rui Wang
  • Jing Lu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 138)


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.


Sparse multi-path channel (SMPC) Regularized orthogonal matching pursuit (ROMP) Sparse approximation 


  1. 1.
    Schreiber WF (1995) Advanced television systems for terrestrial broadcasting: some problems and some proposed solutions. IEEE Proc 83:958–981CrossRefGoogle Scholar
  2. 2.
    Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans ASSP 41(12):3397–3415MATHGoogle Scholar
  3. 3.
    Kocic M et al (1995) Sparse equalization for real time digital underwater acoustic communications. In: Proceedings of the OCEANS’95, San Diego, CA, pp 1417–1422Google Scholar
  4. 4.
    Carbonelli C, Vedantam S, Mitra U (2007) Sparse channel estimation with zero tap detection. IEEE Trans Wirel Commun 6(5):1743–1763CrossRefGoogle Scholar
  5. 5.
    Cotter SF, Rao BD (2002) Sparse channel estimation via matching pursuit with application to equalization. IEEE Trans Commun 50(3):374–377CrossRefGoogle Scholar
  6. 6.
    Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gui G, Wan Q, Huang AM, Jaing CG (2008) Partial sparse multipath channel estimation using L1_LS algorithm (submitted for IEEE TENCON)Google Scholar
  8. 8.
    Bajwa WU, Haupt J, Raz G, Nowak R (2008) Compressed channel sensing. In: Proceedings of the 42nd annual conference information sciences and systems (CISS’08), pp 19–21Google Scholar
  9. 9.
    Donoho D, Elad M, Temlyakov V (2006) Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans Inf Theory 52(1):6–18MathSciNetCrossRefGoogle Scholar
  10. 10.
    Tropp J (2004) Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory 50:2231–2242MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kunis S, Rauhut H (2006) Random sampling of sparse trigonometric polynomials ii—orthogonal matching pursuit versus basis pursuit. Arxiv: preprint math.CA/0604429v2Google Scholar
  12. 12.
    Nguyen NH, Tran TD (2007) The stability of regularized orthogonal matching pursuit algorithm.
  13. 13.
    Cotter SF, Rao BD (2002) Sparse channel estimation via matching pursuit with application to equalization. IEEE Trans Commun 50(3):277–374CrossRefGoogle Scholar
  14. 14.
    Schnass K, Vandergheynst P (2008) Dictionary preconditioning for greedy algorithms. IEEE Trans Inf Theory 56(5):1994–2002MathSciNetGoogle Scholar
  15. 15.
    Candès E, Tao T (2005) Decoding by linear programming. IEEE Trans Inf Theory 51:4203–4215CrossRefGoogle Scholar
  16. 16.
    Candès E, Tao T (2007) The Dantzig selector: statistical estimation when p is much larger than n. Ann Stat 35:2313–2351Google Scholar
  17. 17.
    Bajwa WU, Haupt J, Raz G, Wright S, Nowak R (2007) Toeplitz structured compressed sensing matrices. In: Proceedings of the 14th IEEE/SP workshop on statistical signal processing (SSP’07), pp 294–298Google Scholar

Copyright information

© Springer-Verlag London Limited  2012

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoChina

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