Maximum-Likelihood Soft-Decision Decoding of Linear Codes Using Algorithm A*

本项目旨在开发新的用于线性分组码和卷积码的高效的最大似然软判决译码算法。这里使用的方法是通过一个图将译码问题转化为搜索问题，该图是传输的码的等价码的网格。在人工智能搜索问题中广泛使用的算法A*被用来搜索该图。该搜索由评估函数f来引导，该评估函数f被定义为利用由所接收的矢量提供的信息和所发送的代码的固有属性。该函数f用于显著减小搜索空间，并使这些译码算法的译码努力适应噪声水平。初步结果表明，线性分组码的译码有可能取得突破。卷积码的成功应用将使大约束长度卷积码的最大似然软判决译码在实际通信系统中的使用成为可能。

This project aims to develop new efficient maximum-likelihood soft-decision decoding algorithms for linear block codes and convolutional codes. The approach used here is to convert the decoding problem into a search problem through a graph which is a trellis for an equivalent code of the transmitted code. Algorithm A*, which is widely used in Artificial Intelligence search problems, is used to search through this graph. This search is guided by an evaluation function f defined to take advantage of the information provided by the received vector and the inherent properties of the transmitted code. This function f is used to drastically reduce the search space and to make the decoding efforts of these decoding algorithms adaptable to the noise level. Preliminary results indicate a possible breakthrough in the decoding of linear block codes. Successful application to convolutional codes should make possible the use of maximum-likelihood soft-decision decoders for large constraint length convolutional codes in practical communications systems.