Top > JACIII > Compressive Sensing-Based Adaptive Sparse Multipath Channel Es ...

single-jc.php

JACIII Vol.21 No.1 pp. 153-158
doi: 10.20965/jaciii.2017.p0153
(2017)

Paper:

Views over last 60 days: 1,007

Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation

Beiyi Liu*, Guan Gui**, Shin-ya Matsushita*, and Li Xu*

*Department of Electronics and Information Systems, Akita Prefectural University
84-4 Tsuchiya-Ebinokuchi, Honjo, Akita 015-0055, Japan
E-mails: {m17b018, matsushita, xuli}@akita-pu.ac.jp

**College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications
No.66, New Mofan Rd., Gulou District, Nanjing, China

Received:
July 5, 2016
Accepted:
October 23, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
sparse multipath channel estimation, least mean square, ℓ0-LMS, adaptive filter, compressive sensing
Abstract
Sparse multipath channel estimation has recently attracted significant attention due to the sparsity of the channel in broadband wireless communication. Many algorithms have been proposed for sparse multipath channel estimation. Among them, the least mean square (LMS) algorithm, based on adaptive filter, has attracted much attention due to its low complexity and high robustness. However, LMS is usually degraded by the long training signal, which needs large storage space. This paper proposes an improved method that transmits a circulating, short training signal, samples the received signal at a lower rate, and utilizes LMS with ℓ0-norm (ℓ0-LMS) to estimate the sparse multipath channel. This method can achieve high robustness in additive white Gaussian noise (AWGN), and reduce the sampling rate while needing small storage space for the training signal. Numerical simulations are provided to evaluate the performance of the proposed method.
Cite this article as:
B. Liu, G. Gui, S. Matsushita, and L. Xu, “Compressive Sensing-Based Adaptive Sparse Multipath Channel Estimation,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 153-158, 2017.
Data files:
References
  1. [1] D. Raychaudhuri and N. B. Mandayam, “Frontiers of Wireless and Mobile Communications,” Proc. IEEE, Vol.100, No.4, pp. 824-840, 2012.
  2. [2] F. Adachi and E. Kudoh, “New direction of broadband wireless technology,” Wireless Communications and Mobile Computing, Vol.7, No.8, pp. 969-983, 2007.
  3. [3] B. Widrow and S. D. Stearns, “Adaptive signal processing,” Englewood Cliffs, NJ, Prentice-Hall, Inc., 1985.
  4. [4] Y. Chen and N. Beaulieu, “Performance of collaborative spectrum sensing for cognitive radio in the presence of Gaussian channel estimation errors,” IEEE Trans. Commun., Vol.57, No.7, pp. 1944-1947, 2009.
  5. [5] Y. Chen, Y. Gu, and A. O. Hero, “Sparse LMS for system identification,” IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 3125-3128, 2009.
  6. [6] O. Taheri and S. A. Vorobyov, “Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis,” Signal Processing, Vol.104, pp. 70-79, 2014.
  7. [7] O. Taheri and S. A. Vorobyov, “Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares,” IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 2864-2867, 2011.
  8. [8] G. Gui, W. Peng, and F. Adachi, “Improved adaptive sparse channel estimation based on the least mean square algorithm,” IEEE Wireless Communications and Networking Conf. (WCNC), pp. 3105-3109, 2013.
  9. [9] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, Vol.52, No.4, pp. 1289-1306, 2006.
  10. [10] E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, Vol.52, No.2, pp. 489-509, 2006.
  11. [11] R. Baraniuk, “Compressive Sensing,” IEEE Signal Process. Mag., Vol.24, No.4, pp. 118-121, 2007.
  12. [12] J. Jin, Y. Gu, and S. Mei, “A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework,” IEEE J. Sel. Top. Signal Process., Vol.4, No.2, pp. 409-420, 2010.
  13. [13] J. Haupt, W. U. Bajwa, G. Raz, and R. Nowak, “Toeplitz compressed sensing matrices with applications to sparse channel estimation,” IEEE Trans. Inf. Theory, Vol.56, No.11, pp. 5862-5875, 2010.
  14. [14] C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag., Vol.48, No.11, pp. 164-174, 2010.
  15. [15] J. Huang, C. R. Berger, S. Zhou, and J. Huang, “Comparison of basis pursuit algorithms for sparse channel estimation in underwater acoustic OFDM,” OCEANS 2010 IEEE SYDNEY, Vol.0805, pp. 1-6, 2010.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 01, 2024