Adaptive Channel Equalization Using Multiplicative Neural Network for Rayleigh Faded Channel
P. Sivakumar, N. Chithra, S. N. Sivanandam and M. Rajaram
DOI : 10.3844/jcssp.2011.1646.1651
Journal of Computer Science
Volume 7, Issue 11
Problem statement: Digital transmission over band-limited communication channel largely suffers from ISIS and various noise sources. The presence of ISI and noise causes bit errors in the received signal. Equalization is necessary at the receiver to overcome these channel impairment to recover the original transmitted sequence. Traditionally equalization is considered as equivalent to inverse filtering and implemented using linear-perform under severe distortion conditions when Signal to Noise Ratio (SNR) is poor. Equalization can be considered as a non-linear classification problem and optimum solution is given by Bayesian solution. Non-linear techniques like Artificial Neutral Networks (ANN) are very good choice for non-linear classification problems. Several non-lines are equalizers have been implemented using ANN which outperformed LTE and solved the problem of equalization to the varying degree of sources. Approach: Forward neural network architecture with optimum number of nodes was used to achieve adaptive channel equalization. Summation at each node was replaced by multiplications which result in powerful mapping. The equalizer was tested on Rayleigh fading channel with a BPSK signal. Results: Results showed that proposed equalizer provides simplified architecture and improvement in the bit error rate at various levels of signal to noise ratio for Rayleigh faded channel. Conclusion: A high order feed forward network equalizer with multiplicative neuron is proposed in this study. Use of Multiplication allows direct computing of polynomial inputs and approximation with fewer nodes. Performance comparison in terms of network architecture and BER performance suggest the better classification capability of the proposed MNN equalizer over RRBF.
© 2011 P. Sivakumar, N. Chithra, S. N. Sivanandam and M. Rajaram. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.