Dynamic Behavioral Models for Wideband Wireless Transmitters Stimulated by Complex Signals Using Neural Networks

Authors:
Taijun Liu;Yan Ye;Slim Boumaiza;Fadhel M. Ghannouchi
Affiliations:
College of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China;College of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China;Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada;Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks

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Abstract

In this paper, a time-delay structure is included in the neural network architecture to emulate the memory effects of wideband wireless transmitters. A simplified analysis approach is proposed to illustrate that the Real-Valued Time-Delay Neural Network (RVTDNN) is one of the most promising neural networks for modeling a complex dynamic nonlinear system. Then the RVTDNN is utilized to build the complex signal dynamic behavioral model of a wideband transmitter. Finally, a behavioral model with three-layer RVTDNN is employed in an experimental system to demonstrate the effectiveness of RVTDNNs in mimicking the dynamic behaviors of a wideband wireless transmitter.