Channel Estimation Technique Based on Compressed Sensing

Xiao Feng Hu; Jian Lin Zhu

doi:10.4028/www.scientific.net/AMR.753-755.2594

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Channel Estimation Technique Based on Compressed...

Channel Estimation Technique Based on Compressed Sensing

Abstract:

The traditional channel estimation method in the communication system is based on the multipath channel intensive assume, lead to the spectrum utilization is low, compressed sensing theory provides a new way to solve this problem. The basic theory of compressed sensing is introduced, the feasibility of compressed sensing applied to channel estimation is explored, a detailed analysis several reconstruction algorithm of the compressed sensing channel estimation technologies: MP algorithm, OMP algorithm, CoSaMP. Studies have shown that fewer pilot signal channel estimation method based on compressed sensing theory to achieve the estimated performance comparable to traditional methods, thus improving spectrum efficiency.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

2594-2597

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.2594

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Online since:

August 2013

Authors:

Xiao Feng Hu, Jian Lin Zhu

Keywords:

Channel Estimation, Compressed Sensing Theory, Reconstruction Algorithm

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References

[1] Jiao Licheng, Yang Shuyuan, Liu Fang. HOU Biao, Retrospect and prospect of compressed sensing[J], Electronic Science and Technology (Natural Science Edition), 2011, 39(7): 1651-1662.

Google Scholar

[2] Shang Zhijiang. A study of multipath channel estimation algorithm based on compressed sensing sparse[D], University of Electronic Science and Technology Thesis, 2011.

Google Scholar

[3] Donoho D.L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

DOI: 10.1109/tit.2006.871582

Google Scholar

[4] Tuo Yunying. Based on compressed sensing sparse channel estimation algorithm[D]. Hunan University Thesis, (2011).

Google Scholar

[5] Li Shutao, Wei Dan. Review of compressed sensing[J]. Automatic journal, 2009, 35(7): 1369 -1377.

Google Scholar

[6] Shane F. Cotter and Bhaskar D. Rao. Sparse channel estimation via matching pursuit with application to equalization. IEEE Tran. on Communications, 2002, 50(3): 374-377.

DOI: 10.1109/26.990897

Google Scholar

[7] M.A.T. Figueiredo, R.D. Nowak, and S.J. Wright. Gradient. Gradient projection for sparse reconstruction: Application to Compressed Sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing: Special Issue on Convex Optimization Methods for Signal Processing, 2007, 1(4): 586-598.

DOI: 10.1109/jstsp.2007.910281

Google Scholar