Chase-like Decoding: Test Pattern Design and Performance Analysis

2026-05-08

AI Summary

This work addresses the inefficiency in test pattern set design for soft-input decoding of high-rate BCH codes by proposing a novel algorithm based on high-probability error pattern coverage. Leveraging order statistics, probabilistic modeling of the coverage space, and Monte Carlo simulations, the study systematically evaluates the performance of both structured and unstructured test pattern sets and constructs an optimized set accordingly. Implemented within a Chase-II decoding framework, the proposed method significantly enhances decoding performance, achieving up to a 0.2 dB gain over conventional approaches for high-rate BCH codes, thereby demonstrating its effectiveness and superiority.

Abstract

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.

Problem

• Chase-like decoding
• test pattern design
• algebraic codes
• BCH codes
• soft-input decoding

Innovation

• Chase-like decoding
• test pattern design
• order statistics
• covered space probability
• BCH codes