Comparisons of Short-Prefix Based Channel Estimation in Single-Carrier Communication Systems


In this paper, we compare the performance between channel estimation based on compressed sensing (CS) and time-domain least square (LS) for single-carrier (SC) communication system. Unlike the conventional channel estimation techniques such as frequency domain LS which is used in the condition that the length of pilot sequence is equal to data sequence, the estimation scheme based on CS requires smaller length of pilot sequence. In this paper, the corresponding system structure is presented. Zadoff-Chu sequence is used to generate the pilot sequence, which is shown to perform better in forming measurement matrix of CS than pseudo random sequence. Simulation results demonstrate that channel estimation based on CS achieves a better bit error rate (BER) performance than time domain LS with a smaller pilot sequence and thus raising data rate of the SC communication system.

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Li, H. , Fan, S. , Gong, L. , Cheng, G. and Li, S. (2013) Comparisons of Short-Prefix Based Channel Estimation in Single-Carrier Communication Systems. Communications and Network, 5, 398-402. doi: 10.4236/cn.2013.53B2073.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. Kim, U.-K. Kwon and G.-H. Im, “Pilot Positon Selection and Detection for Channel Estimation of SC-FDE,” IEEE Communications Letter, Vol. 12, No. 5, 2008, pp. 350-352. doi:10.1109/LCOMM.2008.080071
[2] N. Al-Dhahir, “Single-Carrier Frequency-Domain Equalization for Space-Time Block-Coded Transmissions Over Frequency-Selective Fading Channels,” IEEE Communication Letters, Vol. 5, No. 7, 2001, pp. 304-306. doi:10.1109/4234.935750
[3] D. Falconer, S. L. Ariyavisitakul, A. Benyamin-Seeyar and B. Eidson, “Frequency Domain Equalization for Single-Carrier Broadband Wireless Systems,” IEEE Communication Magazine, 2002, pp. 58-66. doi:10.1109/35.995852
[4] M. K. Ozdemir and H. Arslan, “Channel Estimation for Wireless OFDM Systems,” IEEE Communications Surveys & Tutorials, Vol. 9, No. 2, pp. 18-48, 2nd Quarter, 2007.
[5] J. Haupt, W. U. Bajwa, G. Raz and R. Nowak, “Toeplitz Compressed Sensing Matrices with Applications to Sparse Channel Estimation,” IEEE Transaction on Information Theory, Vol. 56, No. 11, 2010, pp. 5862-5875. doi:10.1109/TIT.2010.2070191
[6] E. J. Candes and T. Tao, “Near-optimal Signal Recovery from Random Projections: Univeral Encoding Strategies,” IEEE Transaction on Information Theory, Vol. 52, No. 12, 2006, pp. 5406-5425. doi:10.1109/TIT.2006.885507
[7] C. H. Qi and L. N. Wu, “A Study of Deterministic Pilot Allocation for Sparse Channel Estimation in OFDM System,” IEEE Communication Letters, Vol. 16, No. 5, 2012, pp. 742-744, May, 2012. doi:10.1109/LCOMM.2012.032612.112553
[8] C.-T. Lam and D. D. Falconer, F. Danilo-Lemoine and R. Dinis, “Channel Estimation for SC-FDE Systems Using Frequency Domain Multiplexed Pilots,” Vehicular Technology Conference, 2006, pp. 1-5.
[9] T. Tony Cai and Lie Wang, “Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise,” IEEE Transactions on Information Theory, Vol. 57, No. 7, 2011, pp. 4680-4688. doi:10.1109/TIT.2011.2146090
[10] G. Z. Karabulut, A. Yongacoglu, “Sparse Channel Estimation using Orthogonal Matching Pursuit Algorithm,” Vehicular Technology Conference, pp. 3880-3884, IEEE, 2004.

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