Heuristic Channel Estimation Based on Compressive Sensing in LTE Downlink Channel

Lin Wan, Min Wang, Lifeng Su, Jun Wu

College of Electronics and Information Engineering, Tongji University, Shanghai, China.
College of Electronics and Information Engineering, Tongji University, Shanghai, China; School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.

DOI: 10.4236/cn.2013.53B2018                

Abstract

Pilot-assisted channel estimation has been investigated to improve the performance of OFDM based LTE systems. LS and MMSE method do not perform excellently because they do not consider the inherent sparse feature of wireless channel. The sparse feature of channel impulse response satisfies the requirement of using compressive sensing (CS) theory, which has recently gained much attention in signal processing. Result in the application of using compressive sensing to estimate fading channel. And it achieves a much better performance than that with traditional methods. In this paper, we propose heuristic channel estimation based on CS in LTE Downlink channel. According to the feature of recovery algorithm in CS, we design a modified pilot placement method. CS recovery algorithms for channel estimation don’t consider the statistics character of channel. So we proposed an optimization method which combines the CS and noise reduction. First we get initial channel statistics obtained by LS. Let the channel statistics as the heuristic information input of CS recovery algorithm. Then we perform CS recovery algorithm to estimate channel. Simulation results show this approach significantly reduces the complexity of channel estimation and get a better mean square error (MSE) performance.

Keywords

Channel Estimation; Compressed Sensing; Sparse Channel; LTE; Noise Reduction

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	W. G. Jeon, K. H. Paik and Y. S. Cho, “An Efficient Channel Estimation Technique for Ofdm Systems with Transmitter Diversity,” In Personal, Indoor and Mobile Radio Communications, 2000. PIMRC 2000, The 11th IEEE International Symposium on, Vol. 2, pp. 1246-1250.
[2]	J.-J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. Ola Borjesson, On Channel Estimation in Ofdm Systems, In Vehicular Technology Conference, 1995 IEEE 45th, Vol. 2, pp. 815-819.
[3]	C. Mehlfuhrer, S. Caban and M. Rupp, “An Accurate and Low Complex Channel Estimator for Ofdm Wimax,” In Communications, Control and Signal Processing, 2008, ISCCSP 2008, 3rd International Symposium on, pp. 922-926.
[4]	M.-H. Hsieh and C.-H. Wei, “Channel Estimation for Ofdm Systems Based on Comb-type Pilot Arrangement in Frequency Selective Fading Channels,” Consumer Electronics, IEEE Transactions on, Vol. 44, No. 1, pp. 217-225.
[5]	A. M. Sayeed, Sparse Multipath Wireless Channels: Modeling and implications. 2006.
[6]	E. J. Candes, J. Romberg and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information,” Information Theory, IEEE Transactions on, Vol. 52, No. 2, pp. 489-509.
[7]	S. Zhang, J. Kang, Y. C. Song and N. N. Wang, “An Optimization for Channel Estimation Based on Compressed Channel Eensing,” In Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing (SNPD), 2012 13th ACIS International Conference on, pp. 597-602.
[8]	H. Zamiri-Jafarian, M. J. Omidi and S. Pasupathy, Improved Channel Estimation Using Noise Reduction for Ofdm Systems. In Vehicular Technology Conference, 2003.VTC 2003-Spring, The 57th IEEE Semiannual, Vol. 2, pp. 1308-1312.
[9]	E. J. Candes and T. Tao, “Decoding by Linear Programming. Information Theory,” IEEE Transactions on, Vol. 51, No. 12, pp. 4203-4215.
[10]	D. L. Donoho, “Compressed Sensing. Information Theory,” IEEE Transactions on, Vol. 52, No. 4, pp. 1289-1306.
[11]	J. A. Tropp and A. C. Gilbert, “Signal Recovery from Random Measurements via Orthogonal Matching Pursuit,” Information Theory, IEEE Transactions on, Vol. 53, No. 12, pp. 4655-4666.
[12]	W. Dai and O. Milenkovic, “Subspace Pursuit for Compressive Sensing Signal Reconstruction,” Information Theory, IEEE Transactions on, Vol. 55, No. 5, pp. 2230-2249.
[13]	D. Needell and J. A. Tropp. Cosamp: Iterative Signal Recovery from Incomplete and Inaccurate Samples. Technical Report, California Institute of Technology, Pasadena, 2008.
[14]	C. H. Qi and L. N. Wu, “Optimized Pilot Placement for Sparse Channel Estimation in Ofdm Systems, Signal Processing Letters, IEEE, Vol. 18, No. 12, 2011, pp. 749-752.