基于广义MLP/ bp的MIMO DFEs克服带限信道中ISI和ACI

2007 International Symposium on VLSI Design, Automation and Test (VLSI-DAT) Pub Date : 2007-04-25 DOI:10.1109/VDAT.2007.373221

Terng-Ren Hsu, Chi-Shi Chen, Terng-Yin Hsu, Chen-Yi Lee

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引用次数: 5

摘要

在这项工作中，我们基于带反向传播算法的广义多层感知器神经网络(generalized MLP/BP)来构建多输入多输出(MIMO)决策反馈均衡器(dfe)。该方案用于恢复有线平行带限信道中失真的非归零(NRZ)数据。仿真结果表明，该设计能够恢复严重失真的NRZ数据，抑制码间干扰(ISI)、相邻信道干扰(ACI)和背景噪声。与一组LMS DFE和基于MLP/ bp的MIMO DFE相比，在有线并行带限制信道中实现了更好的BER性能，其中数据速率是信道带宽的十倍。

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Generalized MLP/BP-based MIMO DFEs for Overcoming ISI and ACI in Band-limited Channels

In this work, we base on generalized multi-layered perceptron neural networks with backpropagation algorithm (generalized MLP/BP) to construct multi-input multi-output (MIMO) decision feedback equalizers (DFEs). The proposal is used to recover distorted nonreturn-to-zero (NRZ) data in wireline parallel band-limited channels. From the simulations, we note that the proposed design can recover severe distorted NRZ data as well as suppress intersymbol interference (ISI), adjacent channel interference (ACI) and background noise. The better BER performance as compared to a set of LMS DFEs and an MLP/BP-based MIMO DFE is achieved in the wireline parallel band-limited channels where the data rate is ten times as much as the channel bandwidth.

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来源期刊

2007 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)

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