Neural networks-based turbo equalization of a satellite communication channel

H. Abdulkader, B. Benammar, C. Poulliat, M. Boucheret, N. Thomas
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引用次数: 7

Abstract

This paper proposes neural networks-based turbo equalization (TEQ) applied to a non linear channel. Based on a Volterra model of the satellite non linear communication channel, we derive a soft input soft output (SISO) radial basis function (RBF) equalizer that can be used in an iterative equalization in order to improve the system performance. In particular, it is shown that the RBF-based TEQ is able to achieve its matched filter bound (MFB) within few iterations. The paper also proposes a blind implementation of the TEQ using a multilayer perceptron (MLP) as an adaptive model of the nonlinear channel. Asymptotic analysis as well as reduced complexity implementations are also presented and discussed.
基于神经网络的卫星通信信道turbo均衡
提出了一种应用于非线性信道的基于神经网络的turbo均衡(TEQ)方法。基于卫星非线性通信信道的Volterra模型,推导出一种软输入软输出(SISO)径向基函数(RBF)均衡器,该均衡器可用于迭代均衡,以提高系统性能。特别地,证明了基于rbf的TEQ能够在几次迭代内实现其匹配滤波器界(MFB)。本文还提出了一种使用多层感知器(MLP)作为非线性信道自适应模型的盲实现TEQ。渐近分析和降低复杂度的实现也被提出和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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