Novel Adaptive Channel Equalization Algorithms by Statistical Sampling

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.





References:
[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.