PRACTICAL CONSIDERATIONS OF GREEDY COMPRESSED SAMPLING METHODS APPLICATION FOR OFDM CHANNEL ESTIMATION
DOI:
https://doi.org/10.15588/1607-3274-2018-4-16Keywords:
OFDM, multipath channel, channel estimation, compressed samplingAbstract
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.
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.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2019 V. V. Kotlyarov, A. A. Shpylka
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
