An Improved Computational Model for Adaptive Communication Channel Estimation
Keywords:
Adaptation algorithm, Computational complexity, Memory load, convergence rate, Partial updateAbstract
Channel estimation is an important and necessary function performed by modern wireless receivers. The goal of channel estimation is to measure the effects of the channel on known or partially known transmission. The usual practice in acquiring knowledge about a channel is to model the channel and then acquire the parameters involved in the model. This paper proposes a variable partial update model for adaptive communication channel estimation with a view to improving signal error at the receiver station. The proposed model is composed of finite impulse response transversal adaptive filter and least mean square adaptation algorithm. The performance of the proposed model was compared with the full update model. The evaluation results indicated that the proposed model performed better than the full update model in terms of computational complexity, memory load, and convergence rate.References
C. S. Douglas, and W. Pan, Exact Expectation Analysis of the LMS Adaptive Filter, IEEE Transaction on Signal Processing, 43(12),2863-2871, 1995. http://dx.doi.org/10.1109/78.476430
M. Godavarti and A. O. Hero, Analysis of the Sequential Partial Update LMS Algorithm, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing,pp. 3857-3860, 2001. http://dx.doi.org/10.1109/icassp.2001.940685
S. Mullins and C. Heneghan, Alternative Least Mean Square Adaptive Filter Architectures for Implementation on Field Programmable Gate Arrays, Department of Electronic and Electrical Engineering, University College Dublin available at http://195.134.67.70/eurasip/proceedings/eusipco/2002/articles/paper389.pdf, 2002.
S. Singh, R.K. Bansal, and S. Bansal, Improved Channel Estimation with Auto-Regressive Prewhitening Techniques for Color Inputs, International Conference on Next generation Communication System: ICONGENCOM-06, pp. 9-14, 2006.
Y. Li and W. Xinan, A Modified VS LMS Algorithm, IEEE Transaction on Signal Processing, Vol. 2, No. 5, 615-618, 2005.
S. Diggavi, B. Chong, and A. Paulraj, An Interference Suppression Scheme with Joint Channel Data Estimation, IEEE Journal on Communication, Vol. 17, No.11,465-469, 1998.
J. Sanubari, Fast Convergence LMS Adaptive Filters Employing FuzzyPartial Updates, IEEE Transaction on Signal Processing, Vol. 4, pp. 1334-1337, 2003.
S. Jo, J. Choi and Y. Lee, Modified Leaky LMS Algorithm for Channel Estimation in DSCDMA System, IEEE Communications Letters, Vol. 6, No 5, 202-204 2002. http://dx.doi.org/10.1109/4234.1001664
M. Gadhiok, Channel Estimation for Fast Fading Environment, IEEE Transaction on Signal Processing, Vol. 19, pp. 14-22, 2004.
K. Egiazarian, P. Kuosmanen, and R. C. Bilcu, Variable Step Size LMS Adaptive Filters for CDMA Multiuser Detection, Vol.17 pp. 259-264, 2004.
R. Bilcu, P. Kuosmanen and C. Rusu, A Novel Complementary Variable Step LMS Algorithm, IEEE Transaction on Signal Processing, Vol. 23 pp. 13-19, 2000.
V. Debrunner and D. Zhou, Hybrid Filtered Error LMS Algorithm: Another Alternative to Filtered-x LMS, IEEE Transactions on Circuit and System, Vol. 53, No. 3,pp.653-661, 2006. http://dx.doi.org/10.1109/TCSI.2005.859574
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