DOI: https://doi.org/10.15588/1607-3274-2018-4-16

PRACTICAL CONSIDERATIONS OF GREEDY COMPRESSED SAMPLING METHODS APPLICATION FOR OFDM CHANNEL ESTIMATION

V. V. Kotlyarov, A. A. Shpylka

Abstract


Context. Traditionally a large number of pilot carriers are utilized to acquire channel state information in OFDM based systems.
A larger number of pilot carriers gives better channel state estimation but leads to lower spectrum efficiency of the system.
Objective. Primary objective of this paper is to look at the practical aspects of the application of novel CS-based channel
estimation technique, that can achieve estimation quality on reduced training data, in context of real pilot aided OFDM systems.
Method. A novel technique CS enables representation of sparse signals using fewer samples as compared to its original size.
Exploiting the sparse nature of channel impulse response of multipath channels, we apply the CS technique for channel estimation in
pilot aided OFDM system based on ISDB-T standard.
Results. In this paper, we consider two the most popular CS-based recovery algorithms – OMP and CoSaMP. The MSE
performance metrics are given for both CS-based channel estimation algorithms. Simulations results demonstrate that CoSaMP
provides more stable results while requires more pilot carriers than OMP to achieve good estimation quality. Both algorithms require
a priori knowledge of channel sparsity level but CoSaMP is much more sensitive to a correctness of this information.
Conclusions. The compressed sampling approach shows the impressive capability of channel impulse response recovery from a
significantly smaller amount of pilot carriers than traditional linear methods require. However, the need of sparsity knowledge by the
most popular CS recovery methods seriously limits the applicability of these algorithms in real OFDM receivers. Nevertheless, CSbased
channel estimation is a promising technique which worth further investigation to overcome this limitation.

Keywords


OFDM; multipath channel; channel estimation; compressed sampling

References


Proakis J. G. Digital communications 4ed. New York,

McGraw-Hill, 1983, 1002 p.

Van de Beek J-J., Edfors O., Sandell M., et al. On Channel Estimation in OFDM Systems, Vehicular Technology: 45th international conference, Chicago, 25–28 july 1995: proceedings. Chicago, IEEE, 1995, Vol. 2, pp. 815–819. DOI: 10.1109/VETEC.1995.504981

Li Y. Pilot-Symbol-Aided Channel Estimation for OFDM in Wireless Systems, IEEE Transactions on Vehicular

Technology, 2000, Vol. 49, Issue 4, p. 1207-1215.

DOI: 10.1109/25.875230

Wang X., Wu Y., Chouinard J. Y. Modified Channel

Estimation Algorithms for OFDM Systems with Reduced

Complexity, Signal processing: 7th international

conference, Beijing, 31 august – 4 september 2004:

proceedings. Beijing, IEEE, 2004, Vol. 2. P. 1747–1751.

DOI: 10.1109/ICOSP.2004.1452558 1765. Khan M. Z. Low-Complexity ML Channel Estimation Schemes for OFDM, Conference on networks: 13 th international conference, Kuala Lumpur, 16–18 november 2005: proceedings. Kuala Lumpur, IEEE, 2005, Vol. 2, pp. 607–612. DOI: 10.1109/ICON.2005.1635572

Zhou Y., Herdin M., Sayeed A. M., Bonek E. Experimental study of MIMO channel statistics and capacity via the virtual channel representation [Electronic resource]. Access mode:

https://dfs.semanticscholar.org/266c/3a3e8228381a9335df

d868ba5d0d2803c38.pdf

Donoho D. L Compressed sensing, IEEE Transactions on Information Theory, 2006, Vol. 52, No. 4, pp. 1289–1306. DOI: 10.1109/TIT.2006.871582

Candes E. J., Wakin M. B. An Introduction To Compressive Sampling, IEEE Signal Processing Magazine, 2008, Vol. 25, Issue 2, pp. 21–30.

DOI: 10.1109/MSP.2007.914731

Baraniuk R. G. Compressive Sensing[Lectures Notes], IEEE Signal Processing Magazine, 2007, Vol. 24, Issue 4,

pp. 118–121. DOI: 10.1109/MSP.2007.4286571

Berger C. R., Wang Z. H., Huang J. Z. et al. Application of compressive sensing to sparse channel estimation, IEEE Communication Magazine, 2010, Vol. 48, Issue 11, pp. 164–174. DOI: 10.1109/MCOM.2010.5621984

Cotter S. F., Rao B. D. Sparse channel estimation via

matching pursuit with application to equalization, IEEE

Transaction on Communication, 2002, Vol. 50, Issue 3,

pp. 374–377. DOI: 10.1109/26.990897

Maechler P., Greisen P., Sporrer B. et al. Implementation of greedy algorithms for LTE sparse channel estimation, Conference Signals, System and Computers: 44th Asilomar Conference, Pacific Grove, 7–10 November 2010: proceedings. Pacific Grove, IEEE, 2010, pp. 400–405. DOI: 10.1109/ACSSC.2010.5757587

Tropp J. A., Gilbert A. C. Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions of Information Theory, 2007, Vol. 53, Issue 12, pp. 4655–4666. DOI: 10.1109/TIT.2007.909108

Needell D., Tropp J. A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples, Applied and Computational Harmonic Analysis, 2009, Vol. 26, Issue 3, pp. 301–321. DOI: 10.1016/j.acha.2008.07.002

Transmission System for Digital Terrestrial Television

Broadcast: standard v. 2.2. [Effective from 18 March 2014]. ARIB, 2014, 195 p.

Universal mobile telecommunications systems; Deployment aspects: technical report: 3GPP TR 25.943 v14.0.0/ ETSI, 2017, 15 p.


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