Abstract
In this paper a novel fully complex multiplicative neural network (MNN) algorithm is proposed to extract Quadrature Amplitude Modulation (QAM) signals when passed through a non linear channel in the presence of noise. The inputs, weights, activation functions and the output of the proposed MNN are complex valued. The training algorithm for the multilayer feed forward fully complex MNN is derived. The equalizer is tested on 4, 16 and 32 QAM signals and compared with split complex feed forward MNN equalizer. The proposed equalizer is implemented on nonlinear and nonminimum phase stationary channel. The fast converging algorithm gives lower bit error rate performance even in the presence of substantial noise.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Patra, J.C., Beng, P.W., Chaudhari, N.S., Das, A.: Nonlinear Channel Equalization with QAM Signal using Chebyshev Artificial Neural Network. In: Proc. International Joint Conference on Neural Networks, Montreal, Canada, July 31 - August 4, pp. 3214–3219 (2005)
Chen, S., Mulgrew, B., Grant, P.M.: A Clustering Technique for Digital Communications Channel Equalization using Radial Basis Function Networks. IEEE Trans. Neural Netw. 4(4), 570–579 (1993)
Kechriotis, G., Zervas, E., Manolakos, E.S.: Using Recurrent Neural Networks for Adaptive Communication Channel Equalization. IEEE Trans. Neural Netw. 5(2), 267–278 (1994)
You, C., Hong, D.: Adaptive Equalization using the Complex Backpropagation Algorithm. In: IEEE International Conference on Neural Networks, Washington, DC, USA, June 3-6, vol. 4, pp. 2136–2141 (1996)
Chen, S., Mclaughlin, S., Mulgrew, B.: Complex-valued Radial Basis Function Network, Part II: Application to Digital Communications Channel Equalization. Signal Processing 36, 165–188 (1994)
Cha, I., Kassam, S.A.: Channel Equalization using Adaptive Complex Radial Basis Function Networks. IEEE J. Sel. Area Communication 13(1), 122–131 (1995)
Deng, J., Sundararajan, N., Saratchandran, P.: Communication Channel Equalization using Complex-valued Minimal Radial Basis Function Neural Networks. IEEE Transactions on Neural Networks 13(3), 687–697 (2002)
Ming-Bin, L., Guang-Bin, H., Saratchandran, P., Sundararajan, N.: Fully Complex Extreme Learning Machine. Neurocomputing 68, 306–314 (2005)
Giles, C.L., Maxwell, T.: Learning, Invariance and Generalization in High-order Neural Networks. Applied Optics 26(23), 4972–4978 (1987)
Schmitt, M.: On the Complexity of Computing and Learning with Multiplicative Neurons. Neural Computing 14(2), 241–301 (2002)
Yadav, R.N., Kalra, P.K., John, J.: Time Series Prediction with Single Multiplicative Neuron Model. Applied Soft Computing 7, 1157–1163 (2007)
Yadav, R.N., Singh, V., Kalra, P.K.: Classification using single neuron. In: IEEE Int. Conf. on Industrial Informatics, Banff, Alberta, Canada, August 21-24, pp. 124–129 (2003)
Georgiou, G., Koutsougeras, C.: Complex Domain Backpropagation. IEEE Trans. Circuits Systems II: Analog Digital Signal Process. 39(5), 330–334 (1992)
Kim, T., Adali, T.: Fully Complex Backpropagation for Constant Envelope Signal Processing. In: IEEE Signal Processing Society Workshop, Sydney, Australia, December 11-13, pp. 231–240 (2000)
Kim, T., Adali, T.: Complex Backpropagation Neural Network using Elementary Transcendental Activation Functions. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Salt Lake City, UT, USA, May 7-11, vol. 2, pp. 1281–1284 (2001)
Kavita, B., Yadav, R.N., Shrivastava, S.C.: Complex Channel Equalization using Polynomial Neuron Model. In: Proc. IEEE 3rd Int. Symposium on Information Technology, Kuala Lumpur, Malaysia, August 26-29, pp. 771–775 (2008)
Kavita, B., Yadav, R.N., Shrivastava, S.C., Singh, K.V.P.: A Compact Pi Network for Reducing Bit Error Rate in Dispersive FIR Channel Noise Model. International J. of Electronics, Circuits and Systems 3(3), 150–153 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Burse, K., Yadav, R.N., Shrivastava, S.C. (2010). Fully Complex Multiplicative Neural Network Model and Its Application to Channel Equalization. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-12990-2_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
eBook Packages: EngineeringEngineering (R0)