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 perform up to 0.2$\,$dB better for high-rate BCH codes than commonly used test pattern sets.

翻译：类Chase译码算法是代数码软输入译码中的常用选择。本文通过三种方法评估不同测试模式集的性能。对于具有特定结构的测试模式集（如Chase-II测试模式及最大对数权重以内的模式），我们采用基于顺序统计量的方法。任意测试模式集的性能通过计算覆盖空间概率以及直接蒙特卡罗仿真来评估。基于尽可能覆盖高概率错误模式的思想，我们提出了一种测试模式集设计算法，对于高码率BCH码，该算法比常用测试模式集性能提升可达0.2分贝。