适应性学习信念传播用于解码纠错代码
Alireza Tasdighi1, Mansoor Yousefi1
1Telecom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
适应加权信念传播 (WBP) 解码器为每个接收到的单词动态调整权重,显著提高线性块代码的错误率. 这种方法在各种应用中比静态WBP具有显著的优势.
科学领域:
- 编码理论
- 数字通信
- 机器学习
背景情况:
- 权重信念传播 (WBP) 是线性块代码的解码技术.
- 传统的WBP使用静态,离线优化的权重.
- 需要适应性方法来动态地提高解码性能.
研究的目的:
- 引入和研究适应性加权信念传播 (WBP) 解码器.
- 评估适应性WBP变体与静态WBP的性能.
- 在实际的沟通场景中展示适应性WBP的有效性.
主要方法:
- 解开一个信任传播代码的坦纳图.
- 为循环网络分配和优化边缘权重.
- 开发具有离散权重的并行WBP解码器.
- 使用神经网络实现双阶段解码器来动态测量重量.
主要成果:
- 适应式WBP解码器实现比静态WBP更低的比特错误率 (BER).
- 在各种代码 (BCH,极,QC-LDPC) 和通道条件 (AWGN) 中观察到性能改善.
- 在光纤通道的连接代码应用中,自适应WBP提供了0.8dB的编码增益.
结论:
- 适应式WBP提供了与静态WBP相比的显著性能提升,用于线性块代码解码.
- 拟议的自适应解码器保持可比的计算复杂性和解码延迟.
- 适应式WBP是提高数字通信系统可靠性的有希望的技术.
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