Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 274897, 10 pages
http://dx.doi.org/10.1155/2014/274897
Research Article

Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

1Department of Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, Japan
2Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3Department of Communications Engineering, Tohoku University, Sendai 980-8579, Japan

Received 2 February 2014; Accepted 26 May 2014; Published 24 June 2014

Academic Editor: Serkan Eryílmaz

Copyright © 2014 Guan Gui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Adachi and E. Kudoh, “New direction of broadband wireless technology,” Wireless Communications and Mobile Computing, vol. 7, no. 8, pp. 969–983, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. A. M. Sayeed, “Deconstructing multiantenna fading channels,” IEEE Transactions on Signal Processing, vol. 50, no. 10, pp. 2563–2579, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Yue, K. J. Kim, J. D. Gibson, and R. A. Iltis, “Channel estimation and data detection for MIMO-OFDM systems,” in Proceeding of the IEEE Global Telecommunications Conference (GLOBECOM '03), vol. 2, pp. 581–585, San Francisco, Calif, USA, December 2003. View at Scopus
  4. M. Biguesh and A. B. Gershman, “Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals,” IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 884–893, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. T.-H. Chang, W.-C. Chiang, Y.-W. P. Hong, and C.-Y. Chi, “Training sequence design for discriminatory channel estimation in wireless MIMO systems,” IEEE Transactions on Signal Processing, vol. 58, no. 12, pp. 6223–6237, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. S. He, J. K. Tugnait, and X. Meng, “On superimposed training for MIMO channel estimation and symbol detection,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 3007–3021, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. T.-H. Pham, Y.-C. Liang, and A. Nallanathan, “A joint channel estimation and data detection receiver for multiuser MIMO IFDMA systems,” IEEE Transactions on Communications, vol. 57, no. 6, pp. 1857–1865, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. J. Wang, Z. Han, and K. J. R. Liu, “A MIMO-OFDM channel estimation approach using time of arrivals,” IEEE Transactions on Wireless Communications, vol. 4, no. 3, pp. 1207–1213, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Tauböck, F. Hlawatsch, D. Eiwen, and H. Rauhut, “Compressive estimation of doubly selective channels in multicarrier systems: leakage effects and sparsity-enhancing processing,” IEEE Journal on Selected Topics in Signal Processing, vol. 4, no. 2, pp. 255–271, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, “Compressed channel sensing: a new approach to estimating sparse multipath channels,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1058–1076, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Wang, G. Gui, Z. Zhang, T. Tang, and J. Jiang, “A novel sparse channel estimation method for multipath MIMO-OFDM systems,” in Proceeding of the 74th IEEE Vehicular Technology Conference (VTC-Fall '11), pp. 1–5, San Francisco, Calif, USA, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Dai, Z. Wang, and Z. Yang, “Compressive sensing based time domain synchronous OFDM transmission for vehicular communications,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. 460–469, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. L. Dai, Z. Wang, and Z. Yang, “Next-generation digital television terrestrial broadcasting systems: key technologies and research trends,” IEEE Communications Magazine, vol. 50, no. 6, pp. 150–158, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. N. Czink, X. Yin, H. Özcelik, M. Herdin, E. Bonek, and B. H. Fleury, “Cluster characteristics in a MIMO indoor propagation environment,” IEEE Transactions on Wireless Communications, vol. 6, no. 4, pp. 1465–1475, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Hayashi, M. Nagahara, and T. Tanaka, “A user's guide to compressed sensing for communications systems,” IEICE Transactions on Communications, vol. 96, no. 3, pp. 685–712, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. E. J. Candès, “The restricted isometry property and its implications for compressed sensing,” Comptes Rendus Mathematique, vol. 346, no. 9, pp. 589–592, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Amini, M. Unser, and F. Marvasti, “Compressibility of deterministic and random infinite sequences,” IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5193–5201, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Gui, W. Peng, and F. Adachi, “Improved adaptive sparse channel estimation based on the least mean square algorithm,” in Proceeding of the IEEE Wireless Communications and Networking Conference (WCNC '13), pp. 3105–3109, Shanghai, China, April 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. G. Gui and F. Adachi, “Adaptive sparse channel estimation for time-variant MIMO-OFDM systems,” in Proceeding of the 9th International Wireless Communications & Mobile Computing Conference (IWCMC '13), pp. 1–6, Cagliari, Italy, July 2013.
  22. G. Gui, S. Kumagai, A. Mehbodniya, and F. Adachi, “Variable is good: adaptive sparse channel estimation using VSS-ZA-NLMS algorithm,” in Proceeding of the International Conference on Wireless Communications and Signal Processing (WCSP '13), pp. 291–295, Hangzhou, China, October 2013.
  23. G. Gui and F. Adachi, “Improved adaptive sparse channel estimation using least mean square algorithm,” EURASIP Journal on Wireless Communications and Networking, vol. 2013, no. 1, pp. 1–18, 2013. View at Google Scholar
  24. E. J. Candès, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted 1 minimization,” Journal of Fourier Analysis and Applications, vol. 14, no. 5-6, pp. 877–905, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Chen, Y. Gu, and A. O. Hero III, “Sparse LMS for system identification,” in Proceeding of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '09), pp. 3125–3128, Taipei, Taiwan, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. Huang, G. Gui, A. Huang, D. Xiang, and F. Adachi, “Regularization selection method for LMS-type sparse multipath channel estimation,” in Proceeding of the 19th Asia-Pacific Conference on Communications (APCC '13), pp. 655–660, Bali Island, Indonesia, August 2013.
  27. G. Gui, A. Mehbodniya, and F. Adachi, “Least mean square/fourth algorithm for adaptive sparse channel estimation,” in Proceeding of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '13), pp. 1–5, London, UK, September 2013.
  28. B. Widrow and P. N. Stearns, Adaptive Signal Processing, Prentice Hall, New Jersey, NJ, USA, 4 edition, 1985.
  29. H.-C. Shin, A. H. Sayed, and W.-J. Song, “Variable step-size NLMS and affine projection algorithms,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 132–135, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Cho and S. W. Kim, “Variable step-size normalized LMS algorithm by approximating correlation matrix of estimation error,” Signal Processing, vol. 90, no. 9, pp. 2792–2799, 2010. View at Publisher · View at Google Scholar · View at Scopus