Euclidean-Distance Decoding for Low-Complexity Short Codes
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Solution Overview
Problem
Current decoding algorithms for ultra-reliable low-latency communications (URLLC) face challenges in achieving high performance while maintaining low complexity, particularly for short codes with strong error correction capabilities, as existing methods like OSD and GRAND either have high complexity or performance loss at low code rates.
Innovation Solution
The proposed decoding method, Joint Guessing Error Pattern Decoding (JGEPD), processes received sequences and generator matrices to obtain hard-decision information sequences and codewords, determines error patterns based on minimum Euclidean distance, and post-processes these to output an optimal decoding sequence, with a limited number of iteration times to reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ordered statistic decoding (OSD) algorithm is used to achieve maximum likelihood decoding performance, then decoding performance is improved, but algorithm complexity increases
Solution Approach 1:
The decoding process is divided into three distinct stages: preprocessing to obtain hard-decision information sequence and codeword, error pattern obtaining, and post-processing to output optimal decoding sequence. This segmentation allows each stage to be optimized independently, reducing overall complexity while maintaining ML decoding performance.
Solution Approach 2:
Hard-decision decoding is performed in advance to obtain initial information sequence and codeword before the main error pattern search. This preliminary action reduces the search space for subsequent error pattern generation, significantly lowering the complexity of the overall decoding process while preserving decoding performance.
2Reliability
If blind query is performed in decoding process, then decoding performance may be improved, but number of query times increases and complexity increases
Solution Approach 1:
The Euclidean distance metric provides continuous feedback during the error pattern search process. By calculating and comparing Euclidean distances between received sequences and candidate codewords, the algorithm can systematically identify the most likely transmitted sequence, reducing blind queries while maintaining performance.
Solution Approach 2:
The algorithm changes the search parameter from blind random queries to systematic Euclidean distance-based error pattern generation. This parameter change transforms the search process into a directed optimization problem, significantly reducing the number of queries needed while improving decoding reliability.
Data Source
AI summary
Disclosed are a decoding method and a decoding device. In the method, a received sequence and a generator matrix are processed to obtain a hard-decision information sequence and a hard-decision codeword; an error pattern is determined according to the hard-decision information sequence and the hard-decision codeword; then, the error pattern is post-processed and an optimal decoding sequence is output as a decoding result, wherein the optimal decoding sequence is a decoding sequence with a minimized Euclidean distance. According to the present disclosure, the complexity of the algorithm is reduced while the effective decoding performance can be ensured.