---
title: "Learning to Decode Linear Codes with Generalized Belief Propagation Network"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06874v1"
permalink: "https://feed.astralor.com/items/itm_1518c5ecfa5012e5d8d0cfda"
published: "2026-11-04T18:29:31.000Z"
score: 56
channel: "ai"
tags: ["Learning","Decode Linear Codes","Generalized Belief Propagation","Network Error","Recently","Error"]
---

# Learning to Decode Linear Codes with Generalized Belief Propagation Network

## At a glance

Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical …

## Key points

- Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels.
- Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms.
- In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning capability.

## Full text

Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning capability. The architecture uses cascaded variable-node updates and learned element-wise message scaling while retaining the Tanner graph structure. We also introduce two training strategies, progressive training and high-SNR training, that improve decoding accuracy. Experimental results on several linear codes show that GBPNet achieves lower bit error rates than conventional belief propagation and the evaluated state-of-the-art neural decoders.

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