Abstract
In this paper, we propose a wavelet neural network suitable for reducing nonlinear distortion introduced by a traveling wave tube amplifier (TWTA) over multicarrier systems. Parameters of the proposed network are identified using an hybrid training algorithm, which adapts the linear output parameters using the least square algorithm and the nonlinear parameters of the hidden nodes are trained using the gradient descent algorithm. Computer simulation results confirm that the proposed wavelet network achieves a bit error rate performance very close to the ideal case of linear amplification.
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Rodriguez, N., Cubillos, C., Duran, O. (2007). Wavelet Network for Nonlinearities Reduction in Multicarrier Systems. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_4
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DOI: https://doi.org/10.1007/978-3-540-73055-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73054-5
Online ISBN: 978-3-540-73055-2
eBook Packages: Computer ScienceComputer Science (R0)