Performance of Cellular Neural Network Based Channel Equalizers

Öz

Abstract—In this paper, a popular dynamic neural network structure called Cellular Neural Network (CNN) is employed as a channel equalizer in digital communications. It is shown that, this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled, simple neural network containing only 25 neurons (cells) with a neighborhood of r = 2 , thus including only 51 weight coefficients. Furthermore, a special technique called repetitive codes in equalization process is also applied to the mentioned CNN based system to show that the two-dimensional structure of CNN is capable of processing such signals, where performance improvement is observed. Simulations are carried out to compare the proposed structures with minimum mean square error (MMSE) and multilayer perceptron (MLP) based equalizers.

Anahtar Kelimeler

• Cellular Neural Networks,channel equalization,MLP equalizer,MMSE equalizer

Kaynakça

1. C. Berrou, A. Glavieux, P. Thitimajshima, “”Near Shannon limit errorcorrectingcoding and decoding: Turbo-codes,” in Proceeding of EEEInternational Conference on Communications, Geneva, Switzerland,Nov. 30 - Dec. 4 1993, pp. 1064–1070.
2. M.K. Lee, K. Yang, “Scheduling for an adaptive number of iterationsin turbo equalizers combined with LDPC decoders,” IEEE T Commun,vol. 58, no. 10, pp. 2759–2764, 2010.
3. M. Tuchler, R. Koetter, A. Singer, “Turbo equalization: Principles andnew results,” IEEE T Commun, vol. 50, no. 5, pp. 754–767, 2002.
4. S. Talakoub S., L. Sabeti, B. Shahrrava, M. Ahmadi, “An improved maxlog-MAP algorithm for turbo decoding and turbo equalization,” IEEE TInstrum Meas, vol. 56, no. 3, pp. 1058–1063, 2007.
5. P. J.G., Digital Communications 4th ed. McGraw Hill, 2001.
6. J. Lee, C. Beach, N. Tepedelenlioglu, “A practical radial basis functionequalizer,” IEEE T Neural Networ, vol. 10, no. 2, pp. 450–455, 1999.
7. L. Biao, BL. Evans , “Channel equalization by feed forward neuralnetworks,” in In: Proceeding of IEEE International Symposium onCircuits and Systems, Orlando, FL, USA, May 30 - June 2 1999, pp.587–590.
8. R. Perfetti, “CNN for fast adaptive equalization,” J Circ Theor, vol. 21,no. 2, pp. 165–175, March 1993.

9. A. Ozmen, B. Tander, “Channel equalization with cellular neural networks,” in In: IEEE Mediterranean Electrotechnical Conference,Valletta, Malta, April 2010, pp. 1597–1599.
10. L.O. Chua, L. Yang, “A Cellular neural networks: Theory,” IEEE TransCAS, vol. 35, no. 10, pp. 1257–1272, 1988.
11. G. Costantini, D. Casali, M. Carota, “Detection of moving objects in 2-Dimages based on a CNN algorithm and density based spatial clustering,”WSEAS Trans CAS, vol. 4, no. 5, pp. 440–447, 2005.
12. K. S. M., Fundamentals of Statistical Signal Processing: EstimationTheory. Prentice Hall, 1993.
13. T. Tozek, T. Roska, L.O. Chua, “ Genetic algorithm for CNN templatelearning,” IEEE Trans. CAS I: Fund. Theo. and Apps, vol. 40, no. 6, pp.392–402, 1993.