论文标题
ML辅助通信的位错误和块错误率训练
Bit Error and Block Error Rate Training for ML-Assisted Communication
论文作者
论文摘要
即使在通信中广泛使用了机器学习（ML）技术，但如何培训通信系统的问题令人惊讶地受到关注。在本文中，我们表明，常用的二进制跨凝结（BCE）损失是未编码系统的明智选择，例如，用于训练ML辅助数据检测器，但在编码系统中可能不是最佳选择。我们提出了针对最小化块错误率和SNR降至降低的新损失功能，这是一种新颖的方法，可训练通信系统，以在一系列信噪比上进行最佳性能。通过Nvidia sionna中的模拟显示了所提出的损失函数以及SNR脱水的效用。
Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.
