用神经网络设计通信系统，不依赖传统编码，在低功耗下实现抗噪传输。
Learning Robust Representations for Communications over Noisy Channels
- 基于互信息和码字距离设计损失函数，提升抗噪能力。
- 在严格功率约束下，块错误率显著降低。
- 新编码器结构来自Barlow Twins，适合通信场景的端到端学习。
我们探索使用全连接神经网络（FCNN）设计端到端通信系统，不参考现有经典通信模型或纠错编码。本工作仅依赖信息论与机器学习工具。研究了基于互信息和码字间成对距离的多种损失函数对在严格功率约束下生成鲁棒传输表示的影响。此外，提出一种受Barlow Twins框架启发的新编码器结构。结果表明，通过在随机噪声功率水平下迭代训练并最小化块错误率，可获得最佳误码性能。
原文摘要 · Abstract (English)
We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications models or error control coding. This work relies solely on the tools of information theory and machine learning. We investigate the impact of using various cost functions based on mutual information and pairwise distances between codewords to generate robust representations for transmission under strict power constraints. Additionally, we introduce a novel encoder structure inspired by the Barlow Twins framework. Our results show that iterative training with randomly chosen noise power levels while minimizing block error rate provides the best error performance.
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