Abstract
This chapter applies the natural gradient (NG) descent for adaptive identification of nonlinear channels with memory. The nonlinear channel is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(.). The adaptive system is a neural network which is composed of a linear adaptive filter Q followed by a two-layer memoryless nonlinear neural network (NN). It is shown that the NG learning method significantly outperforms the ordinary gradient descent method in terms of convergence speed and mean squared error (MSE) performance. The chapter studies the sensitivity of the different NN parameters to the natural gradient descent and gives applications to channel tracking and to the modeling of high power amplifiers.
1 In this chapter, the use of the word ‘backpropagation’ (BP) without specifying the gradient descent method, refers to the classical BP algorithm trained with the ordinary gradient descent. (section 2.1).
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Ibnkahla, M. (2004). Nonlinear Channel Identification Using Natural Gradient Descent: Application to Modeling and Tracking. In: Soft Computing in Communications. Studies in Fuzziness and Soft Computing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45090-0_3
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DOI: https://doi.org/10.1007/978-3-540-45090-0_3
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