AUTHORS: Gamal Mabrouk Abdel-Hamid, Reham S. Saad
Download as PDF
ABSTRACT: A new blind channel estimation method for long term evaluation (LTE) based on combining advantages of denoising property of wavelet transform (WT) with blind estimation capability of independent component analysis (ICA) called wavelet denoising of ICA (WD-ICA) is presented. This new method increases the spectral efficiency compared to training based methods, and provides considerable performance enhancement over conventional ICA methods. The conventional blind channel estimation methods based on ICA is performed individually for each orthogonal frequency division multiplexing (OFDM) subcarrier. To reduce complexity of implementation of WD-ICA method, channel interpolation is used. This method is presented for multiple-input-multiple-output (MIMO) downlink LTE system. WD-ICA method is compared to conventional ICA methods and the Performance is evaluated by calculating normalized mean square error (NMSE) and bit error rate (BER). WD-ICA method as compared to the other known ICA channel estimation methods has lower complexity, lower value of NMSE, and lower value of BER, which indicates the superiority of the proposed method.
KEYWORDS: -LTE, Blind channel estimation, WT, ICA, OFDM, MIMO
REFERENCES:
[1] Yufei.jiang, xuzhu, enggeelim and yihuang, Orthogonal Sequences Based Multi-CFO Estimation and Semi-Blind ICA Based Equalization for Multiuser Comp Systems, Journal of Computer Science &Information Systems, Vol. 9, No. 4, Dec. 2012, pp. 1385:1406.
[2] Yufei Jiang; Xu Zhu; Enggee Lim; Linhao Dong; Yi Huang, Low-Complexity Independent Component Analysis Based SemiBlind Receiver for Wireless Multiple-Input Multiple-Output Systems, International Journal of Design, Analysis and Tools for Circuits and Systems, Vol. 2, No. 2, Aug. 2011, pp. 91-98.
[3] J. Gao, X. Zhu and A. K. Nandi, Independent Component Analysis for Multiple-Input Multiple-Output Wireless Communication Systems, Journal of Signal Processing, Vol. 91, No. 4, 2011, pp. 607-623.
[4] Chiu Shun Wong, Dragan Obradovic, Independent Component Analysis (ICA) for Blind Equalization of Frequency Selecrive Channels, 14th IEEE workshop Neural Networks for Signal Processing, 2003, pp.419- 428.
[5] Obradovic, D., Madhu, N., Szabo, A.,Wong, C.S, Independent Component Analysis for Semi-Blind Signal Separation in MIMO Mobile Frequency Selective Communication Channels, IEEE International Joint Conference on Neural Networks, 2004, pp. 53–58.
[6] M. Sifuzzaman, M.R. Islam and M.Z. Ali, Application of Wavelet Transform and its Advantages Compared to Fourier Transform, Journal of Physical Sciences, Vol. 13, Oct. 2009, pp. 121-134.
[7] Karanpreet Kaur, Disceret Wavelet Transform based OFDM System Using Convolutional Encoding, M.S. thesis, Department of Electronics and Communication Engineering, Thapar university, Patiala. 2014.
[8] Mahesh Kumar Gupta, Sarika Shrivastava, A.S. Raghuvanshi and S.Tiwari, Channel Estimation for Wavelet Based OFDM System, in proc. 2011Int. Conf. on Devices and Commun. (ICDeCom), Feb. 2011, pp. 1-4.
[9] E. Hari Krishna, KosarajuSivani, K. Ashoka Reddy, OFDM Channel Estimation and Equalization UsingMulti Scale Independent Component Analysis, IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Feb. 2015, pp. 1-5.
[10] Oomke Weikert, Christian Klünder, UdoZölzer, Semi-Blind Equalization of Wireless MIMO Frequency Selective Communication Channels, Independent Component Analysis and Blind Signal Separation, 6th International Conference, Mar. 2006, pp. 422-429.
[11] G. ThavasiRaja , P. Krishna Chaitanya and R. Malmathanraj, Performance Analysis of Independent Component Analysis Algorithms for Multi-user Detection of DS-CDMA, International Journal of Computer Applications, Vol. 39, No. 11, Feb. 2012, pp. 34-37.
[12] Luciano Sarperi, Xu Zhu, Asoke K. Nandi, Blind OFDM Receiver Based on Independent Component Analysis for Multiple-Input Multiple-Output Systems, Journal of IEEE Transactions on Wireless Communications, Vol. 6, No. 11, Nov. 2007, pp. 4079-4089.
[13] Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, New York: John Wiley & Sons, 2001.
[14] Ella Bingham and Aapo Hyvarinen, A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals, International Journal of Neural Systems, Vol. 10, No. 1, Feb. 2000, pp. 1-8.
[15] HyvärinenA, Oja E, A Fast Fixed-Point Algorithm for Independent Component Analysis, Journal of Neural Computation, Vol. 9, No.7, 1997, pp. 1483–1492.
[16] AapoHyvärinen and ErkkiOja, Independent Component Analysis: Algorithms and Applications, Journal of Neural Networks, 2000, pp. 411-430.
[17] JariMiettinen, Klaus Nordhausen, HannuOja, Sara Taskinen, Fast equivariant JADE,IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013,pp. 6153-6157.
[18] J.-F. Cardoso and A. Souloumiac, Blind Beamforming for Non Gaussian Signals, Journal of IEE Proceedings-F, Vol.140, No.6, 1993, pp. 362–370.
[19] J.-F. Cardoso, High-Order Contrasts for Independent Component Analysis, Journal of Neural Computation, Vol. 11, No.1, Jan.1999, pp.157–192.
[20] D.N. Rutledge, D. Jouan-Rimbaud Bouveresse, Independent Components Analysis with the JADE Aalgorithm, Journal of Trends in Analytical chemistry, Vol. 50, Oct. 2013, pp.22–32.
[21] http://www.mathworks.com/examples/wavelet/ 636-de-noising-signals-and-images.
[22] X. Cai and G. B. Giannakis, Error Probability Minimizing Pilots for OFDM with M-PSK Modulation Over Rayleigh-Fading Channels, Journal of IEEE Transactions onVechicular Technology, Vol. 53, No. 1, Jan. 2004, pp. 146–155.