Soft Decision Decoding For Block Codes Using Artificial Neural Networks

使用人工神经网络对块码进行软决策解码

• 批准号: 9216686
• 负责人: Stephen Wicker
• 金额: $ 12.53万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-08-01 至 1996-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216686&HistoricalAwards=false
• 关键词: Soft; Decision; Decoding; Block; Codes

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. ***

9216686威克上世纪80年代初，人们对人工神经网络(ANN)重新产生兴趣，S推动了人们对其在许多工程领域的应用的研究，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其能够模拟各种功能。通过使用诸如反向传播之类的训练算法来增强这种仿真，该训练算法允许某些类型的人工神经网络“学习”期望的功能。研究了人工神经网络在分组差错控制码软判决译码中的应用。在过去的两年里，实验研究表明，某些人工神经网络可以对某些分组码进行差错控制译码。到目前为止的实验结果表明，ANN可以进行硬判决译码，但软判决译码的结果非常有限。在本研究中，首先将差错控制问题转化为泛函分析。译码被视为从连续接收信号空间到离散信息字空间的映射。从而将译码设计转化为函数逼近问题。下一步是使用一类非常有前途的人工神经网络--前馈神经网络(FFNN)，以高效地实现解码功能。可以从几何上观察FFNN的操作，其中网络中的每一层将接收的信号空间分割成越来越低的维子空间，最终在提供对传输的信息比特的估计的输出决策层中。因此，函数逼近方法允许理想的软判决译码，而FFNN设计使用分组码中的代数几何冗余来降低译码复杂度。这些解码器的性能将通过使用ANN训练技术来进一步提高，所述ANN训练技术将在解码器运行时将输入层判决度量与信道条件匹配。其结果将是一系列高效的软判决解码器，适用于各种应用中的各种分组码。***