Reliability-Based OSD Decoding with Systematic Error Pattern Selection
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Solution Overview
Problem
The traditional Ordered Statistic Decoding (OSD) algorithm for ultra-reliable low-latency communication (URLLC) achieves high decoding performance but at the cost of high complexity, which is unsustainable as code length increases and communication reliability is not guaranteed for short codes.
Innovation Solution
A decoding method that involves permutation processing on receiving sequences and generator matrices based on bit reliability, followed by Gaussian elimination to obtain a systematic generator matrix, and hard-decision decoding, with target error patterns selected to generate a decoding result, reducing complexity while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Ordered Statistic Decoding (OSD) algorithm is used, then decoding performance is improved, but decoding complexity increases
Solution Approach 1:
The decoding process is divided into multiple stages: permutation processing based on reliability metrics, Gaussian elimination to obtain systematic generator matrix, hard-decision decoding, and iterative error pattern selection. Each stage processes only necessary portions of the data, avoiding exhaustive search while maintaining performance.
Solution Approach 2:
The algorithm performs preliminary permutation processing on the receiving sequence and generator matrix according to bit reliability before decoding. This preliminary organization of data based on reliability metrics enables more efficient subsequent processing and reduces the search space for error patterns.
2Reliability
If code length increases, then decoding performance should improve, but complexity increases and reliability is not guaranteed for short codes
Solution Approach 1:
The algorithm adapts the error pattern selection process based on code length and reliability requirements. For short codes, it selectively targets specific error patterns rather than exhaustive search, while for longer codes it leverages the systematic structure to maintain performance with controlled complexity.
Solution Approach 2:
The decoding process dynamically adjusts the number of iterations and error pattern selection based on whether the preset end condition is achieved. The algorithm continues iterating only as long as necessary to achieve the required reliability, avoiding unnecessary computational overhead.
Data Source
AI summary
Disclosed are a decoding method and a decoding device. The decoding method includes: performing a permutation processing on a receiving sequence and a generator matrix to obtain a permuted receiving sequence and an intermediate generator matrix according to a reliability of each bit of the receiving sequence; performing a Gaussian elimination processing on the intermediate generator matrix to obtain a systematic generator matrix generator matrix; performing a hard-decision decoding on the permuted receiving sequence to obtain a hard-decision decoding sequence; in response to determining a preset decoding end condition is not achieved, selecting a target error pattern from an error pattern set; and generating a decoding result based on the target error pattern, the hard-decision decoding sequence, and the systematic generator matrix. The decoding method can ensure the decoding performance and reduce the decoding complexity.