🤖 AI Summary
This work addresses the challenge of efficiently decoding conventional BCH codes, whose dense parity-check matrices hinder high-performance iterative decoding in modern communication systems. The authors propose a quasi-belief propagation decoder that integrates prior knowledge—including channel noise variance, cyclic code structure, and parity redundancy—and, for the first time, extends extrinsic information transfer (EXIT) chart analysis to parameter optimization for high-density parity-check codes. Convergence is accelerated through novel message expansion and merging mechanisms, while mutual information evolution analysis enables efficient parallel decoding. Experimental results demonstrate that the proposed method significantly improves BCH code performance across various code rates and lengths, closely approaching that of LDPC codes with equivalent parameters, thereby confirming its generality and robustness.
📝 Abstract
This paper proposes a quasi-belief propagation decoder for BCH codes that systematically integrates domain knowledge--specifically, channel noise variance, the cyclic property of the codes, and the deliberate redundancy in their parity-check matrices--to enable efficient iterative decoding. We rigorously formalize this parallelizable decoder within an information-theoretic framework by tracking mutual information evolution through the constituent variable and check decoders, thereby validating the use of scattered EXIT charts as a tool for optimizing the decoder's parameters. At each iteration, an input dilation operation expands the set of messages, while a subsequent merging operation accelerates mutual information growth, ensuring rapid convergence. The proposed decoder achieves decoding performance approaching that of LDPC codes with comparable rate and blocklength, effectively pioneering the feasible deployment of BP-like decoding for high-density parity-check codes. The generality and robustness of the scheme are demonstrated through extensive simulations across codes of varying rates and blocklengths.