基于神经网络的Volterra核提取放大器行为建模

Jelena Misic, V. Markovic, Z. Marinković
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引用次数: 7

摘要

在无线通信系统中,放大器的非线性失真问题是一个极具挑战性的问题。基于放大器行为模型的线性化技术似乎非常有前途,因此建立一个合适的非线性模型至关重要。一种严谨的非线性建模方法是使用Volterra级数,然而Volterra系数的计算是一项复杂且耗时的任务。在本文中,将提出一种简单而先进的方法来提取Volterra核。利用具有合适激活函数的前馈时滞神经网络的参数推导出三阶Volterra核。
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Volterra kernels extraction from neural networks for amplifier behavioral modeling
In wireless communication systems the amplifier non-linear distortion problems are extremely challenging. The linearization techniques based on behavioral models of amplifiers, seem to be very promising, therefore developing a suitable non-linear model is of the crucial importance. A rigorous approach for non-linear modeling is using the of Volterra series, however the calculation of the Volterra coefficients is a complex and time-consuming task. In this paper, an easy and advanced approach for extraction of the Volterra kernels will be presented. The third order Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function.
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