Problem
Research questions and friction points this paper is trying to address.
ensemble decoding
linear block codes
affine subcodes
error correction performance
short block length
Affine Subcode Ensemble Decoding of Linear Block Codes
🤖 AI Summary
This work addresses the challenge of achieving both high decoding performance and low latency in short-blocklength scenarios, where conventional suboptimal decoders such as belief propagation (BP) often fall short. The paper proposes affine Subcode-ensemble Decoding (aSCED), which for the first time integrates strict affine subcodes into an ensemble decoding framework. By parallelly combining decoders for linear and strict affine subcodes, aSCED enhances error-correction capability. The method employs an affine-subcode-based BP update rule and leverages LDPC codes, BCH codes, and high-performance parity-check matrix construction algorithms. Experimental results demonstrate that aSCED significantly outperforms existing ensemble decoding schemes across multiple code families, approaching maximum-likelihood performance with only 64 BP decoding paths.
📝 Abstract
In the short block length regime, ensemble decoding schemes with their inherently parallel structure can improve error correction performance and reduce latency compared to stand-alone suboptimal decoders such as belief propagation (BP). In this work, we introduce affine subcode ensemble decoding (aSCED), which uses an ensemble of decoders operating on linear block codes and both linear and strictly affine subcodes. This generalizes the recently proposed subcode ensemble decoding (SCED), which is restricted to linear subcodes. We derive BP update rules for affine subcodes and show that aSCED simplifies ensemble design compared to SCED, multiple bases BP, and automorphism ensemble decoding. Monte-Carlo simulations of two low-density parity-check codes and two Bose-Chaudhuri-Hocquenghem (BCH) codes demonstrate improved error correction performance of aSCED over competing existing ensemble schemes. Notably, for one BCH code, when combining ensemble design with algorithms for constructing high-performance parity-check matrices, aSCED achieves near-maximum likelihood performance using only 64 BP decoding paths.
Innovation
Methods, ideas, or system contributions that make the work stand out.
affine subcode ensemble decoding
belief propagation
linear block codes
error correction performance
short block length
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