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Iterative Decoding of Concatenated Convolutional Codes: Implementation Issues

机译:级联卷积码的迭代解码:实现问题

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摘要

This tutorial paper gives an overview of the implementation aspects related to turbo decoders, where the term turbo generally refers to iterative decoders intended for parallel concatenated convolutional codes as well as for serial concatenated convolutional codes. We start by considering the general structure of iterative decoders and the main features of the soft-input soft-output algorithm that forms the heart of iterative decoders. Then, we show that very efficient parallel architectures are available for all types of turbo decoders allowing high-speed implementations. Other implementation aspects like quantization issues and stopping rules used in conjunction with buffering for increasing throughput are considered. Finally, we perform an evaluation of the complexities of the turbo decoders as a function of the main parameters of the code
机译:本教程文件概述了与Turbo解码器有关的实现方面,其中术语Turbo通常指的是旨在用于并行级联卷积码以及串行级联卷积码的迭代解码器。我们首先考虑迭代解码器的一般结构以及构成迭代解码器核心的软输入软输出算法的主要特征。然后,我们表明非常有效的并行体系结构可用于所有类型的Turbo解码器,从而可以实现高速实现。考虑了与量化问题和停止规则结合使用的其他实现方面,这些规则与缓冲一起用于增加吞吐量。最后,我们根据代码的主要参数对turbo解码器的复杂性进行评估

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