Chase-like Decoding: Test Pattern Design and Performance Analysis
2026-05-08 • Information Theory
Information Theory
AI summaryⓘ
The authors study methods to improve Chase-like decoding algorithms, which help fix errors in certain types of algebraic codes. They test how well different sets of error patterns work by using three ways: a math-based approach for structured sets, probability calculations, and computer simulations. Based on covering more likely errors, the authors propose a new way to pick these patterns that improves performance slightly for high-rate BCH codes. This helps make decoding a bit more reliable. Their work compares different test pattern strategies systematically.
Chase decodingalgebraic codestest pattern setsorder statisticslogistic weightMonte Carlo simulationerror patternsBCH codessoft-input decoding
Authors
Tim Janz, Simon Obermüller, Andreas Zunker, Stephan ten Brink
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.