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Channel Equalisation of BPSK and QPSK Signal Using Higher Order Neural Networks


Affiliations
1 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, India
2 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, India
     

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A high order feed forward neural network architecture like Multiplicative Neural Network (MNN), Sigma-Pi Network (SPN) and Improved Sigma-Pi Network (ISPN) are used for adaptive channel equalization in this paper. The replacement of summation by multiplication at the neuron results in more powerful mapping because of its capability of processing higher-order information from training data. The equalizer is tested on Rayleigh fading channel with BPSK and QPSK signals. Performance comparison with back propagation (BPN) neural network shows that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of energy per power spectral density ratios for a Rayleigh fading channel, and also in terms of learning rate and training time.

Keywords

Channel Equalization, MNN, SPN, ISPN, BPN, Rayleigh Fading Channel.
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  • Channel Equalisation of BPSK and QPSK Signal Using Higher Order Neural Networks

Abstract Views: 275  |  PDF Views: 1

Authors

S. Manjula
Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, India
M. Malleswaran
Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, India

Abstract


A high order feed forward neural network architecture like Multiplicative Neural Network (MNN), Sigma-Pi Network (SPN) and Improved Sigma-Pi Network (ISPN) are used for adaptive channel equalization in this paper. The replacement of summation by multiplication at the neuron results in more powerful mapping because of its capability of processing higher-order information from training data. The equalizer is tested on Rayleigh fading channel with BPSK and QPSK signals. Performance comparison with back propagation (BPN) neural network shows that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of energy per power spectral density ratios for a Rayleigh fading channel, and also in terms of learning rate and training time.

Keywords


Channel Equalization, MNN, SPN, ISPN, BPN, Rayleigh Fading Channel.



DOI: https://doi.org/10.36039/ciitaas%2F3%2F6%2F2011%2F106953.272-277