doi: 10.7763/IJCTE.2009.V1.63
Nonlinear Fading Channel Equalization of BPSK Signals Using Multiplicative Neuron Model
- maulana azad national institute of technology, bhopal, india.
Abstract
A high order feed forward neural network architecture with optimum number of nodes is used for adaptive channel equalization in this paper. The replacement of summation at each node by multiplication 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 signals. Performance comparison with recurrent radial basis function (RRBF) neural network show that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of signal to noise ratios for a Rayleigh fading channel.
Keywords
- channel equalization
- BPSK signal
- multiplicative neuron
- Rayleigh channel
How to Cite
Kavita Burse, R. N. Yadav, and S. C. Shrivastava, "Nonlinear Fading Channel Equalization of BPSK Signals Using Multiplicative Neuron Model," International Journal of Computer Theory and Engineering, vol. 1, no. 4, pp. 398-402, 2009. https://doi.org/10.7763/IJCTE.2009.V1.63
Copyright & License
Copyright © 2009 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).