Soft-Decision Linear Code Decoding With MRB-Based Fast OSD
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Solution Overview
Problem
The high computational complexity of ordered statistics decoding (OSD) for soft decision-based linear code decoding methods hinders efficient decoding performance, necessitating a method to reduce operational complexity while maintaining performance.
Innovation Solution
The proposed method involves sorting received signals, obtaining hard decision signals, identifying most reliable bases, and using an error vector to calculate a cost function, with predefined speeding conditions to determine a permuted and corrected codeword candidate, allowing for the omission of unnecessary calculations based on preconfigured thresholds and signal-to-noise ratio considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ordered statistics decoding (OSD) is used for soft decision-based linear code decoding, then decoding performance is improved, but operational complexity increases significantly
Solution Approach 1:
The patent segments the decoding process into distinct stages: sorting received signals by magnitude, performing hard decision on sorted signals, identifying most reliable bases (MRBs), and generating codeword candidates. This segmentation allows the system to process only relevant portions of the data at each stage, reducing overall computational complexity while maintaining decoding performance.
Solution Approach 2:
The patent extracts and processes only the most reliable bases (MRBs) from the hard decision signals rather than processing all signals equally. By identifying and focusing computation on the top k most reliable bases, the system significantly reduces the number of operations required while preserving the essential decoding functionality.
Solution Approach 3:
The patent implements partial action by using a predefined speeding condition that determines when to stop generating codeword candidates. Instead of exhaustively searching all possible candidates, the system stops when the speeding condition is met (comparing cost function values), performing only the necessary computations to achieve acceptable decoding performance.
2Measurement precision
If traditional OSD method is used, then decoding accuracy is maintained, but processing speed is slow
Solution Approach 1:
The patent performs preliminary sorting of received signals by magnitude before the main decoding process. This preliminary action organizes the data in advance, allowing subsequent steps to efficiently access and process only the most relevant signals without repeated sorting operations, thereby improving processing speed while maintaining accuracy.
Solution Approach 2:
The patent introduces dynamic control through the predefined speeding condition that adapts the decoding process based on intermediate results. The cost function comparison dynamically determines when to terminate candidate generation, allowing the system to balance between processing speed and decoding accuracy based on the specific input characteristics.
Data Source
AI summary
Disclosed are a method and an apparatus for fast decoding a linear code based on soft decision. The method may comprise sorting received signals in a magnitude order to obtain sorted signals; obtaining hard decision signals by performing hard decision on the sorted signals; obtaining upper signals corresponding to MRBs from the hard decision signals; obtaining a permuted and corrected codeword candidate using the upper signals and an error vector according to a current order; calculating a cost for the current order using a cost function; determining the permuted and corrected codeword candidate as a permuted and corrected codeword according to a result of comparing the calculated cost with a minimum cost; and determining a predefined speeding condition.