Novel Adaptive Channel Equalization Algorithms by Statistical Sampling
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Novel Adaptive Channel Equalization Algorithms by Statistical Sampling

Authors: János Levendovszky, András Oláh


In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.

Keywords: Cellular Neural Network, channel equalization, communication over fading channels, multiuser communication, spectral efficiency, statistical sampling.

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[1] A. Kapur, M.K. Varanasi, Multiuser Detection for Overloaded CDMA Systems, IEEE Trans. Information Theory, vol. 49, 2003, pp. 1728- 1742.
[2] F. Vanhaverbeke, M. Moeneclaey, Overloaded CDMA systems with displaced binary signatures, EURASIP Journal on W. Comm. and Net., vol. 1, pp. 161-171, 2004;
[3] A.J. Viterbi, CDMA, Principles of spread spectrum communication, Addison-Wesley, 1992.
[4] M.K. Varanasi, B. Aazhang, Near-optimum detection in synchronous code division multiple access system, IEEE Trans. Commun., vol. 39, 1991, pp. 725-736.
[5] R. Lupas and S. Verdu, Linear multiuser detectors for synchronous codedivision multiple-access channels, IEEE Trans. Inform. Theory, vol. 35, 1989, pp. 123-136.
[6] Verdu, S., Shamai, S., Spectral Efficiency of CDMA with Random Spreading, IEEE Transactions on Information Theory, vol. 45, no. 2, 1999, pp 622-640.
[7] J.G. Proakis, Digital Communications, McGrawHill, 2001.
[8] T. S. Rappaport, Wireless Communications, Prentice-Hall, 1996.
[9] A. Sayeed, A. Sendonaris, and B. Aazhang, Multiuser Detection in Fast Fading Multipath Environments, IEEE Journal on Selected Areas in Comm., 1998, pp. 1691-1701.
[10] S. Verdu, Multiuser detection, Cambridge University Pres, 1999.
[11] S. Haykin, Adaptive filter theory, Prentice Hall, 1996.
[12] R. W. Lucky, Automatic equalization for digital communication, Bell Syst. Tech. Journal, vol. 44, pp. 547-588, 1965.
[13] J.G. Proakis, J.H. Miller, An adaptive receiver for digital signaling through channels with intersymbol interference, IEEE Trans. Information Theory, vol. IT-45, pp. 484-497, 1969.
[14] H.V. Poor, S. Verdu, Probability of error in MMSE multiuser detection, IEEE Trans. Commun., vol. 38., 1997, pp. 858-871.
[15] B. Mulgrew, S. Chen, A.K. Samingan, L. Hanzo, Adaptive minimum BER linear multiuser detection for DS-CDMA signals in multipath channels, IEEE Trans. Signal Processing, vol. 49, no.6, 2001, pp. 1240- 1247.
[16] J. Levendovszky, A. Olah, D. Varga, Novel CNN based detection for increased spectral efficiency. 8th IEEE International Biannual Workshop on Cellular Neural Networks and their Applications, 2004, pp. 483-486.
[17] J. Levendovszky, L. Jereb, Adaptive statistical methods in network reliability analysis, 2nd Symposium on Rare Events simulations, 2000.
[18] O.K. Li, J.A. Silvester, Performance Analysis of Networks with Unreliable Components, IEEE Trans. Commun., vol. COM-32, 1984, pp. 1105-1110.
[19] R. Gold, Optimal Binary Sequences for Spread Spectrum Multiplexing, IEEE Transactions on Information Theory, vol. 14. pp 154-156, 1967;
[20] W. G. Teich, M. Seidl, G. Jeney, S. Imre, L. Pap, Code division multiple access communications: multiuser detection based on a recurrent neural network structure, IEEE Trans. Veh. Technol., vol. 46, 1996, pp. 979- 984.
[21] Chua, L.O., Roska T. and Venetianer, P.L., The CNN is as Universal as the Turing Machine. IEEE Trans. on Circuits and Systems, Vol. 40., March, 1993.
[22] L.O. Chua and T. Roska.: The CNN paradigm. IEEE Trans. on Circuits and Systems, Vol. 40, 1993.