AI & ML
Four-Level Quantized Soft Information Aided Hard-Decision Decoder for Product-like Codes
arXiv math.IT0 views
arXiv:2610.21187v1 Announce Type: new Abstract: We propose a novel low-complexity soft-aided hard-decision decoding scheme for product-like codes, termed iterative bounded-distance decoding (iBDD) with graded scaled reliability and bit-flipping (GSRF). This scheme operates on four-level quantized soft information. It incorporates a graded scaled reliability rule to assign decoding confidence to the bounded-distance decoding result, and employs a bit-flipping rule based on bit-level and component-code-level reliability. A selective re-decoding rule is designed to further facilitate the error performance while keeping the number of BDD attempts in reasonably low levels. The decoding parameter design of iBDD-GSRF is investigated for product codes and staircase codes, respectively. Simulation results on both codes verify that iBDD-GSRF achieves a gain of more than 0.4 dB over the conventional iBDD, comparable to several existing unquantized soft-aided decoders.
Source:https://arxiv.org/abs/2610.21187PreviousLWM-TF: A Wireless Foundation Model on 5G NR Time-Frequency Resource GridsNextOn the Subadditivity of Mutual f-Information
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