A Compact Pi Network for Reducing Bit Error Rate in Dispersive FIR Channel Noise Model
During signal transmission, the combined effect of the
transmitter filter, the transmission medium, and additive white
Gaussian noise (AWGN) are included in the channel which distort
and add noise to the signal. This causes the well defined signal
constellation to spread causing errors in bit detection. A compact pi
neural network with minimum number of nodes is proposed. The
replacement of summation at each node by multiplication results in
more powerful mapping. The resultant pi network is tested on six
different channels.
[1] J.C. Patra, R.N. Pal, R. Baliarsingh and G. Panda, "Nonlinear channel
equalization for QAM signal constellation using artificial neural
networks," IEEE Trans. Syst. Man Cybern. B, Cybern., vol. 29 (2), pp.
262-271,1999.
[2] R. N. Yadav, V. Singh and P. K. Kalra," Classification using single
neuron," in Proc. IEEE Int. Conf. on Industrial Informatics, Banff,
Alberta, Canada, Aug. 21-24, 2003, pp. 124-129.
[3] R. N. Yadav, P. K. Kalra and J. John," Time series prediction with
single multiplicative neuron model," Applied soft computing, vol 7, pp
1157-1163,2007.
[4] C.L Giles and T. Maxwell," Learning, invariance and generalization in
high-order neural networks," Applied Optics, vol. 26(23), pp. 4972-
4978, 1987.
[5] M. Schmitt," On the complexity of computing and learning with
multiplicative neurons," Neural Computing, vol. 14(2), pp. 241-301,
2002.
[6] Kavita Burse, R.N. Yadav and S.C. Shrivastava," Complex Channel
Equalization using Polynomial Neuron Model," in Proc. IEEE 3rd Int.
Symposium on Information Technology, Kuala Lumpur, Malaysia, Aug.
26-29, 2008, pp. 771-775.
[7] Wan-De Weng, Che-Shih Yang, Rui-Chang Lin," A channel equalizer
using reduced decision feedback Chebyshev functional link artificial
neural networks," Information Sciences, vol. 177(13), pp. 2642-2654,
2007.
[1] J.C. Patra, R.N. Pal, R. Baliarsingh and G. Panda, "Nonlinear channel
equalization for QAM signal constellation using artificial neural
networks," IEEE Trans. Syst. Man Cybern. B, Cybern., vol. 29 (2), pp.
262-271,1999.
[2] R. N. Yadav, V. Singh and P. K. Kalra," Classification using single
neuron," in Proc. IEEE Int. Conf. on Industrial Informatics, Banff,
Alberta, Canada, Aug. 21-24, 2003, pp. 124-129.
[3] R. N. Yadav, P. K. Kalra and J. John," Time series prediction with
single multiplicative neuron model," Applied soft computing, vol 7, pp
1157-1163,2007.
[4] C.L Giles and T. Maxwell," Learning, invariance and generalization in
high-order neural networks," Applied Optics, vol. 26(23), pp. 4972-
4978, 1987.
[5] M. Schmitt," On the complexity of computing and learning with
multiplicative neurons," Neural Computing, vol. 14(2), pp. 241-301,
2002.
[6] Kavita Burse, R.N. Yadav and S.C. Shrivastava," Complex Channel
Equalization using Polynomial Neuron Model," in Proc. IEEE 3rd Int.
Symposium on Information Technology, Kuala Lumpur, Malaysia, Aug.
26-29, 2008, pp. 771-775.
[7] Wan-De Weng, Che-Shih Yang, Rui-Chang Lin," A channel equalizer
using reduced decision feedback Chebyshev functional link artificial
neural networks," Information Sciences, vol. 177(13), pp. 2642-2654,
2007.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61328", author = "Kavita Burse and R.N. Yadav and S.C. Shrivastava and Vishnu Pratap Singh Kirar", title = "A Compact Pi Network for Reducing Bit Error Rate in Dispersive FIR Channel Noise Model", abstract = "During signal transmission, the combined effect of the
transmitter filter, the transmission medium, and additive white
Gaussian noise (AWGN) are included in the channel which distort
and add noise to the signal. This causes the well defined signal
constellation to spread causing errors in bit detection. A compact pi
neural network with minimum number of nodes is proposed. The
replacement of summation at each node by multiplication results in
more powerful mapping. The resultant pi network is tested on six
different channels.", keywords = "Additive white Gaussian noise, digitalcommunication system, multiplicative neuron, Pi neural network.", volume = "3", number = "2", pages = "322-4", }