Vol. 24

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2011-10-21

Compressive Estimation of Cluster-Sparse Channels

By Guan Gui, Na Zheng, Nina Wang, Abolfazl Mehbodniya, and Fumiyuki Adachi
Progress In Electromagnetics Research C, Vol. 24, 251-263, 2011
doi:10.2528/PIERC11092005

Abstract

Cluster-sparse multipath channels, i.e., non-zero taps occurring in clusters, exist frequently in many communication systems, e.g., underwater acoustic (UWA), ultra-wide band (UWB), and multiple-antenna communication systems. Conventional sparse channel estimation methods often ignore the additional structure in the problem formulation. In this paper, we propose an improved compressive channel estimation (CCE) method using block orthogonal matching pursuit algorithm (BOMP) based on the cluster-sparse channel model. Making explicit use of the concept of cluster-sparsity can yield better estimation performance than the conventional sparse channel estimation methods. Compressive sensing utilizes cluster-sparse information to improve the estimation performance by further mitigating the coherence in training signal matrix. Finally, we present the simulation results to confirm the performance of the proposed method based on cluster-sparse.

Citation


Guan Gui, Na Zheng, Nina Wang, Abolfazl Mehbodniya, and Fumiyuki Adachi, "Compressive Estimation of Cluster-Sparse Channels," Progress In Electromagnetics Research C, Vol. 24, 251-263, 2011.
doi:10.2528/PIERC11092005
http://www.jpier.org/PIERC/pier.php?paper=11092005

References


    1. Bajwa, W. U., 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, 1058-1076, 2010.
    doi:10.1109/JPROC.2010.2042415

    2. Gui, G., Q.Wan, A. M. Huang, and Z. X. Chen, "Sparse multipath channel estimation using dantzig selector algorithm," The 12th International Symposium on Wireless Personal Multimedia Communications, Sendai, 2009.

    3. Czink, N., X. Yin, H. Ozcelik, M. Herdin, E. Bonek, and B. Fleury, "Cluster characteristics in a MIMO indoor propagation environment," IEEE Transactions on Wireless Communications, Vol. 6, No. 4, 1465-1475, 2007.
    doi:10.1109/TWC.2007.348343

    4. Vuokko, L., V. M. Kolmonen, J. Salo, and P. Vainikainen, "Measurement of large-scale cluster power characteristics for geometric channel models," IEEE Transactions on Antennas and Propagation, Vol. 55, No. 11, 3361-3365, 2007.
    doi:10.1109/TAP.2007.908844

    5. Gui, G., Q. Wan, W. Peng, and F. Adachi, "Sparse multipath hannel estimation using compressive sampling matching pursuit algorithm," IEEE VTS APWCS, Gaoxiong, 2010.

    6. Gui, G., Q. Wan, N. N. Wang, and C. Y. Huang, "Fast sparse multipath channel estimation with smooth L0 algorithm for broadband wireless communication systems," Communication and Networks, Vol. 3, No. 1, 1-7, 2010.
    doi:10.4236/cn.2011.31001

    7. Taubock, G. and F. Hlawatsch, "A compressed sensing techniques for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots," IEEE ICASSP, 2885-2888, 2008.

    8. Stojanovic, M., "OFDM for underwater acoustic communications: Adaptive synchronization and sparse channel estimation," IEEE ICASSP, Vol. 1, No. 6, 5288-5291, 2008.

    9. Rontogiannis, A. A. and K. Berberidis, "Efficient decision feedback equalization for sparse wireless channels," IEEE Transactions on Wireless Communications, Vol. 2, No. 3, 570-581, 2003.
    doi:10.1109/TWC.2003.811189

    10. Paredes, J. L., G. R. Arce, and Z. Wang, "Ultra-wideband compressed sensing: Channel estimation," IEEE Journal of Selected Topics in Signal Processing, Vol. 1, No. 3, 383-395, 2007.
    doi:10.1109/JSTSP.2007.906657

    11. Shi, G., J. Lin, X. Chen, F. Qi, D. Liu, and L. Zhang, "UWB echo signal detection with ultra-low rate sampling based on compressed sensing," IEEE Transactions on Circuits and Systems, Vol. 55, No. 4, 379-383, 2008.
    doi:10.1109/TCSII.2008.918988

    12. Zhou, C., N. Guo, and R. C. Qiu, "Time-reversed ultra-wideband (UWB) multiple measured spatial channels," IEEE Transactions on Vehicular Technology, Vol. 58, No. 6, 2884-2898, 2009.
    doi:10.1109/TVT.2008.2012109

    13. Gui, G., A. Mehbodniya, Q. Wan, and F. Adachi, "Sparse signal recovery with OMP algorithm using sensing measurement matrix," IEICE Electronics Express, Vol. 8, No. 5, 285-290, 2011.
    doi:10.1587/elex.8.285

    14. Huang, A. M., G. Gui, Q. Wan, and A. Mehbodniya, "A block orthogonal matching pursuit algorithm based on sensing dictionary," International Journal of the Physical Sciences, Vol. 6, No. 5, 992-999, 2011.

    15. Huang, A. M., G. Gui, Q. Wan, and A. Mehbodniya, "A re-weighted algorithm for designing data dependent sensing dictionary," International Journal of the Physical Sciences, Vol. 6, No. 3, 386-390, 2011.

    16. Eldar, Y. C., P. Kuppinger, and H. Bolcskei, "Block-sparse signals: Uncertainty relations and efficient recovery," IEEE Transactions on Signal Processing, Vol. 58, No. 6, 3042-3054, 2010.
    doi:10.1109/TSP.2010.2044837

    17. Eldar, Y. C. and M. Mishali, "Robust recovery of signals from a structured union of subspaces," IEEE Transactions on Information Theory, Vol. 55, No. 11, 5302-5316, 2009.
    doi:10.1109/TIT.2009.2030471

    18. Cai, T. T. and L. Wang, "Orthogonal matching pursuit for sparse signal recovery with noise," IEEE Transactions on Information Thoeory, Vol. 57, No. 12, 4655-4666, 2011.

    19. Tropp, J. A. and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, Vol. 53, No. 12, 4655-4666, 2007.
    doi:10.1109/TIT.2007.909108

    20. Tropp, J. A., A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Processing, Vol. 86, No. 3, 572-588, 2006.
    doi:10.1016/j.sigpro.2005.05.030

    21. Special issue, , "Sparse approximations in signal and image processing,", http://nips.cc/Conferences/2009/Program/event.php?ID=1878.

    22. Adachi, F., H. Tomeba, and K. Takeda, "Introduction of frequency-domain signal processing to broadband single-carrier transmissions in a wireless channel," IEICE Transactions on Communicationns, Vol. E92-B, No. 9, 2789-2808, 2009.
    doi:10.1587/transcom.E92.B.2789

    23. Biglieri, E., J. Proakis, and S. Shamai, "Fading channels: Information-theoretic and communications aspects," IEEE Transactions on Information Theory, Vol. 44, No. 6, 2619-2692, 1998.
    doi:10.1109/18.720551

    24. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transctions on Information Theory, Vol. 52, No. 2, 489-509, 2006.
    doi:10.1109/TIT.2005.862083

    25. Donoho, D. L., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, 2006.
    doi:10.1109/TIT.2006.871582

    26. Cai, T. T., G. Xu, and J. Zhang, "On recovery of sparse signals via l1 minimization," IEEE Transactions on Information Theory, Vol. 55, No. 7, 3388-3397, 2009.
    doi:10.1109/TIT.2009.2021377

    27. Donoho, D. L. and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Transactions on Information Theory, Vol. 47, No. 7, 2845-2862, 2001.
    doi:10.1109/18.959265

    28. Karabulut, G. Z. and A. Yongacoglu, "Sparse channel estimation using orthogonal matching pursuit algorithm," IEEE 60th Vehicular Technology Conference, IEEE VTC2004-Fall, 3880-3884, 2004.
    doi:10.1109/VETECF.2004.1404804

    29. Tropp, J. A., "Greed is good: Algorithmic results for sparse approximation," IEEE Transactions on Information Theory, Vol. 50, No. 10, 2231-2242, 2004.
    doi:10.1109/TIT.2004.834793