← 研踪 PaperTrailarXiv:2611.15694 · v1
Optimization of Finite Alphabet Message Passing Decoding on Fully Node-Parallel Architectures for 5G LDPC Codes
首次提交 2026-11-12 00:32
版本更新 2026-11-12 00:32
雷达观测 2026-11-13 14:16
摘要
In this paper, we propose a novel design method for the lookup tables (LUTs) of the minimum reconstruction, computation, and quantization (min-RCQ) LDPC code decoding algorithm. The proposed design method uses Bayesian optimization and samples from a subspace of the design space. The novel design method does not require knowledge about the channel or the modulation format and uses the frame error rate (FER) as loss function. The design method is applicable to irregular low-density parity-check (LDPC) codes with puncturing and shortening. Furthermore, the design method creates a set of iteration-agnostic LUTs that can be used over all iterations, which makes this approach suitable for fully node-parallel decoder architectures. We show that our proposed approach achieves a 38.6 % better energy efficiency than a state-of-the-art normalized min-sum (NMS) decoder without loss in error correction performance.
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v1 · 2026-11-12 00:32 · 最新观测版本
Optimization of Finite Alphabet Message Passing Decoding on Fully Node-Parallel Architectures for 5G LDPC Codes
In this paper, we propose a novel design method for the lookup tables (LUTs) of the minimum reconstruction, computation, and quantization (min-RCQ) LDPC code decoding algorithm. The proposed design method uses Bayesian optimization and samples from a subspace of the design space. The novel design method does not require knowledge about the channel or the modulation format and uses the frame error rate (FER) as loss function. The design method is applicable to irregular low-density parity-check (LDPC) codes with puncturing and shortening. Furthermore, the design method creates a set of iteration-agnostic LUTs that can be used over all iterations, which makes this approach suitable for fully node-parallel decoder architectures. We show that our proposed approach achieves a 38.6 % better energy efficiency than a state-of-the-art normalized min-sum (NMS) decoder without loss in error correction performance.
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