Model-based compressed sensing algorithms for MIMO- OFDM channel estimation
About this article
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.https://doi.org/10.1109/JPROC.2010.2042415.
[3] Z. H. Wang, C. R. Berger, J. Z. Huang and S. L. Zhou, “Application for Compressive Sensing to Sparse Channel Estimationâ€, IEEE Communication Magazine, vol. 48, pp.164-174, November 2010.https://doi.org/10.1109/MCOM.2010.5621984.
[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 et.al, “Performance of Multi-user MIMO OFDM Channel Estimation with LS and MMSE for 802.11n Systemsâ€, 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,â€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,â€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.https://doi.org/10.1109/TIT.2006.871582.
[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.https://doi.org/10.1109/TIT.2007.909108.
[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.https://doi.org/10.1109/TIT.2009.2016006.