Guangwen Li

Guangwen Li, Xiao Yu

This paper presents a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes, including LDPC, BCH, and RS codes. The framework introduces several key innovations. A decoding information aggregation model based on a convolutional neural network refines bit-reliability estimates for OSD using the soft-output trajectory of the NMS decoder. An adaptive decoding path for OSD is initialized by the arranged list of the most a priori likely tests algorithm and dynamically updated with empirical data. A sliding-window assisted model enables early termination of test error pattern (TEP) traversal, reducing complexity with minimal performance loss. For short high-rate codes, an undetected error detector identifies erroneous NMS outputs that satisfy parity checks, ensuring they are forwarded to OSD for correction. Extensive simulations on LDPC, BCH, and RS codes demonstrate that the proposed hybrid decoder achieves a competitive trade-off: near-ML frame error rate performance while maintaining advantages in throughput, latency, and complexity over state-of-the-art alternatives. Complexity analysis shows that the average number of OSD TEPs is drastically reduced, and the architecture remains highly parallelizable. An optimization framework is also formulated to balance performance and complexity via parameter tuning.

Guangwen Li, Xiao Yu

It is known belief propagation decoding variants of LDPC codes can be unrolled easily as neural networks after assigning differed weights to message passing edges flexibly. In this paper we focus on how to determine these weights, in the form of trainable paramters, within a framework of deep learning. Firstly, a new method is proposed to generate high-quality training data via exploiting an approximation to the targeted mixture density. Then the strong positive correlation between training loss and decoding metrics is fully exposed after tracing the training evolution curves. Lastly, for the purpose of facilitating training convergence and reducing decoding complexity, we highlight the necessity of slashing the number of trainable parameters while emphasizing the locations of these survived ones, which is justified in the extensive simulation.

Guangwen Li

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.