Soft Decision Decoding For Block Codes Using Artificial Neural Networks

小行星9216686 在20世纪80年代早期，对人工神经网络（ANN）的兴趣的复苏刺激了对它们在许多工程领域中的应用的研究，包括数字信号处理和通信。 人工神经网络的模式识别能力使它们能够模拟各种功能。这种仿真通过使用训练算法（如反向传播）来增强，该算法允许某些类型的ANN“学习”所需的函数。 本文研究人工神经网络在分组差错控制码软判决译码中的应用。 在过去的两年中，实验研究已经证明，某些人工神经网络可以执行一些分组码的差错控制解码。 迄今为止的实验结果表明，人工神经网络可以执行硬判决解码，但软判决解码结果非常有限。 在这项调查中，错误控制问题首先被转化为功能分析的条款。 解码被视为从连续的接收信号空间到离散的信息字空间的映射。 解码器的设计，从而转化为一个问题的功能近似。 下一步是使用一种非常有前途的ANN类，前馈神经网络（FFNN），以有效的方式实现解码功能。 FFNN的操作可以从几何学上来观察，网络中的每一层将接收到的信号空间分割成越来越低维的子空间，最终形成输出判决层，该输出判决层提供对所发送的信息比特的估计。 因此，函数逼近方法允许理想的软判决解码，而FFNN设计使用块码内的代数几何冗余来降低解码器复杂度。 这些解码器的性能将通过使用ANN训练技术进一步增强，ANN训练技术将在解码器操作时将输入层决策度量与信道条件相匹配。 其结果将是一系列有效的软判决解码器，用于各种应用中的各种分组码。 ***

9216686 Wicker The revival of interest in artificial neural networks (ANN's) in the early 1980's has spurred research into their application in many engineering fields, including digital signal processing and communications. The pattern recognition capabilities of ANN's allows them to emulate a variety of functions. This emulation is enhanced through the use of training algorithms, such as back-propagation, that allow some types of ANN's to "learn" desired functions. This research is an investigation into the application of ANN's to the soft decision decoding of block error control codes. In the past two years, experimental studies have demonstrated that certain ANN's can perform error control decoding for some block codes. The experimental results have so far shown that ANN's can perform hard decision decoding, but soft decision decoding results have been extremely limited. In this investigation, the error control problem is first translated into the terms of functional analysis. Decoding is viewed as a mapping from a continuous received signal space onto a discrete information word space. Decoder design is thus translated into a problem of functional approximation. The next step is to use a highly promising class of ANN's, feedforward neural networks (FFNN's), to implement the decoding function in an efficient manner. The operation of FFNN's can be viewed geometrically, with each layer in the network carving up the received signal space into increasingly lower dimensional subspaces, culminating in an output decision layer that provides estimates of the transmitted information bits. The functional approximation approach thus allows for ideal soft decision decoding, while the FFNN design uses the algebro-geometric redundancy within the block code to reduce the decoder complexity. The performance of these decoders will be further enhanced through the use of ANN training techniques that will match input layer decision metrics to channel conditions w hile the decoder is operating. The result will be a series of efficient soft decision decoders for a variety of block codes in a variety of applications. ***