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

机译:轮廓史黛拉图像和深度学习在物理层中的信号识别

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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.
机译:通信系统的快速发展造成前所未有的挑战,例如,处理以实时和细粒度的方式爆炸无线信号。数据驱动机器学习算法的最新进展,尤其是深度学习(DL),表现出解决挑战的巨大潜力。然而,物理层中的波形可能不适用于普遍的经典DL模型,例如卷积神经网络(CNN)和经常性神经网络(RNN),主要接受应用程序中的图像,时间序列和文本数据的格式层。因此,将信号波形之间的间隙弥合到DL可编程数据格式的信号波形之间的差距是相当大的。在本文中,我们开发一个框架,将复值信号波形转换为具有统计显着性的图像,称为轮廓恒星图像(CSI),其可以从原始无线信号波形传达深度级别统计信息,同时以图像数据格式表示。在本文中,我们探讨了几种潜在的应用方案,并提出了有效的基于CSI的解决方案来解决信号识别挑战。我们的调查验证了CSI是一个有希望的方法,用于弥合信号识别和DL之间的差距。

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