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Problem statement: Digital transmission over band-limited communication channel&#13; largely suffers from ISIS and various noise sources. The presence of ISI and noise causes bit errors&#13; in the received signal. Equalization is necessary at the receiver to overcome these channel&#13; impairment to recover the original transmitted sequence. Traditionally equalization is considered as&#13; equivalent to inverse filtering and implemented using linear-perform under severe distortion&#13; conditions when Signal to Noise Ratio (SNR) is poor. Equalization can be considered as a non-linear&#13; classification problem and optimum solution is given by Bayesian solution. Non-linear techniques&#13; like Artificial Neutral Networks (ANN) are very good choice for non-linear classification problems.&#13; Several non-lines are equalizers have been implemented using ANN which outperformed LTE and&#13; solved the problem of equalization to the varying degree of sources. Approach: Forward neural&#13; network architecture with optimum number of nodes was used to achieve adaptive channel&#13; equalization. Summation at each node was replaced by multiplications which result in powerful&#13; mapping. The equalizer was tested on Rayleigh fading channel with a BPSK signal. Results: Results&#13; showed that proposed equalizer provides simplified architecture and improvement in the bit error&#13; rate at various levels of signal to noise ratio for Rayleigh faded channel. Conclusion: A high order&#13; feed forward network equalizer with multiplicative neuron is proposed in this study. Use of&#13; Multiplication allows direct computing of polynomial inputs and approximation with fewer nodes.&#13; Performance comparison in terms of network architecture and BER performance suggest the better&#13; classification capability of the proposed MNN equalizer over RRBF

Adaptive Channel Equalization Using Multiplicative Neural Network for Rayleigh Faded Channel