Chase-like decoding algorithms are a popular choice for soft-input decoding of algebraic codes. In this paper, we evaluate the performance of different test pattern sets using three methods. For test pattern sets with a certain structure such as Chase-II test patterns and patterns up to a maximum logistic weight, we use a method that relies on order statistics. The performance of arbitrary sets of test patterns is evaluated by calculating covered space probabilities and via direct Monte Carlo simulation. Based on the idea of covering as many likely error patterns as possible, we propose an algorithm for the design of test pattern sets which performs up to 0.2\,dB better for high-rate BCH codes than commonly used test patterns.

翻译：类Chase译码算法是代数码软输入译码的一种常用方法。本文通过三种方法评估不同测试图样集的性能。对于具有特定结构的测试图样集（如Chase-II测试图样及最大逻辑权重以内的图样），我们采用基于顺序统计量的方法进行分析。对于任意测试图样集，则通过计算覆盖空间概率和直接蒙特卡洛仿真来评估其性能。基于尽可能覆盖高概率错误图样的思想，我们提出了一种测试图样集设计算法，对于高码率BCH码，该算法性能比常用测试图样提升达0.2分贝。