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.
翻译:尽管在通信中广泛使用机器学习(ML)技术,但如何培训通信系统的问题却很少受到令人惊讶的关注。在本文中,我们表明,在未编码系统中,通常使用的二进制跨叶杆菌(BCE)损失是一个明智的选择,例如,用于培训ML辅助数据探测器,但在编码系统中可能不是最佳选择。我们提出了新的损失功能,目的是尽量减少区块误差率和SNR减量,这是一种新颖的方法,用来培训通信系统,使其在一系列信号到噪音比率的基础上发挥最佳性能。拟议的损失功能以及SNR脱重的作用通过NVIDIA Sionna的模拟来显示。</s>