Model-based compressed sensing algorithms for MIMO- OFDM channel estimation

  • Abstract
  • Keywords
  • References
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  • Abstract

    High data rates on the wireless channel can be achieved by combining orthogonal frequency division multiplexing (OFDM) with multiple input multiple output (MIMO) communication modulation scheme. MIMO-OFDM system impulse response of the channel is approximately sparse. Sparse channelestimation can be done using Compressive Sensing (CS) techniques. In this paper, a low complexity model based CoSaMp Compressive Sensing (CS) algorithm with conventional tools namely Least Square (LS) and Least Mean Square (LMS) are used for MIMO-OFDM channel estimation. Simulation results show amodel based CoSaMP for MIMO-OFDM channel estimation with LMS tool the Normalized Mean Square Error(NMSE)reduced by 34%with very reduced complexity.

  • Keywords

    Sparsity; Compressedsensing; Channelestimation; Compressed Sampling Matching Pursuit; Model-Based Cosamp.

  • References

      [1] Li, Y., “Simplified Channel Estimation for OFDM Systems with Multiple Transmit Antennas,” IEEE Transactions on Communications, vol. 1, pp. 67-75, January 2002.

      [2] W. U. Bajwa, J. Haupt, A. M. Sayeed and R. Nowak,“Compressed channel sensing: a new approach toestimating sparse multipath channels,” Proc. IEEE, vol. 98,no. 6, pp. 1058-1076, June 2010.

      [3] Z. H. Wang, C. R. Berger, J. Z. Huang and S. L. Zhou, “Application for Compressive Sensing to Sparse Channel Estimation”, IEEE Communication Magazine, vol. 48, pp.164-174, November 2010.

      [4] R. F. Song, X. Y. He, , and K. Q. Zhou, “Study ofCompressive Sensing Based Sparse Channel Estimation in OFDM Systems” Journal of Nanjing University of PostsandTelecommunications(Natural Science), vol. 30, no. 2,pp. 60-65, 2011.

      [5] Jung Nam Ba, “Performance of Multi-user MIMO OFDM Channel Estimation with LS and MMSE for 802.11n Systems”, IEEE Int. conf communication and Information Technology, pp. 1-5, .Sept.2009.

      [6] X. Yang, Y. X. Peng, X. F. Zhang, W. B. Wang and B. Wu,“Compressed MIMO-OFDM Channel Estimation,”Communication Technology IEEE InternationalConferenceon, pp.1291-1294, November 2010.

      [7] P N Jayanthi, andS. Ravishankar,"Sparse channel estimation for MIMO-OFDM systems using compressed sensing” IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp: 1060-1064., 2016.

      [8] J. A. Tropp and D. Needell, “Cosamp:iterative signal recovery from incomplete and inaccurate measurements,” Appl. Comput. Harm. Anal., pp. 301–321, 2009.

      [9] Richard G. Baraniuk, Volkan Cevher; Marco F. Duarte; Chinmay Hegde,”Model-Based Compressive Sensing“.,IEEE Transactions on Information Theory Volume: 56, Issue: 4 pp: 1982 – 2001, 2010.

      [10] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol.52, no. 4, pp. 1289–1306, Sep. 2006.

      [11] E. J. Candès, “Compressive Sampling,” in Proc. Int. Congr. Math.,Madrid, Spain, 2006, vol. 3, pp. 1433–1452.

      [12] R. G. Baraniuk, “Compressive sensing” IEEE Signal Processing Mag.,vol. 24, no. 4, pp. 118–120, Jul. 2007, 124.

      [13] E.J Jose et. al. “Signal recovery for random projections,” in Proc. Compute. Imaging III at SPIE Electron. Imaging vol. 5674, pp. 76–86. Jan. 2005.

      [14] Qi and L. Wu, “A hybrid compressed sensing algorithm for sparse channel estimation forMIMO OFDM systems,” in Proc. IEEE Int. Conf.Acoust., Speech, Signal Process., May 2011, pp. 3488–3491.

      [15] J. A. Tropp and A. C. Gilbert, “Signal Recovery From Random Measurements via OMP,” IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655– 4666, Dec. 2007.

      [16] W. Dai and O. Milenkovic, “Subspace Pursuit for Compressive Sensing Signal Reconstruction,” IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230–2249, Apr. 2009.




Article ID: 10030
DOI: 10.14419/ijet.v7i2.4.10030

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