The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such as convolution neural network (CNN) and recurrent neural network (RNN), which mainly accept formats of images, time series, and text data in the application layer. Therefore, it is of considerable interest to bridge the gap between signal waveforms to DL amenable data formats. In this paper, we develop a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waveforms while being represented in an image data format. In this paper, we explore several potential application scenarios and present effective CSI-based solutions to address the signal recognition challenges. Our investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.

中文翻译：

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用于物理层信号识别的轮廓 Stella 图像和深度学习

通信系统的快速发展带来了前所未有的挑战，例如，以实时和细粒度的方式处理爆炸性的无线信号。数据驱动机器学习算法的最新进展，尤其是深度学习 (DL)，显示出应对挑战的巨大潜力。然而，物理层的波形可能不适合流行的经典DL模型，例如卷积神经网络（CNN）和循环神经网络（RNN），它们在应用中主要接受图像、时间序列和文本数据的格式层。因此，弥合信号波形与 DL 兼容数据格式之间的差距具有相当大的意义。在本文中，我们开发了一个框架，将复值信号波形转换为具有统计意义的图像，称为轮廓恒星图像（CSI），它可以从原始无线信号波形中传达深层统计信息，同时以图像数据格式表示。在本文中，我们探索了几个潜在的应用场景，并提出了有效的基于 CSI 的解决方案来解决信号识别挑战。我们的调查证实 CSI 是一种很有前途的方法，可以弥合信号识别和 DL 之间的差距。