Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer

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 article, 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 article, 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.

Automated summary

This article discusses the challenges posed by the rapid development of communication systems and the potential of using deep learning to address these challenges. It presents a framework to transform complex-valued signal waveforms into contour stellar images (CSI) to bridge the gap between signal waveforms and DL amenable data formats, showcasing effective solutions for signal recognition challenges. The investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.