Deep Learning Radio Frequency Signal Classification with Hybrid Images

• URL: http://arxiv.org/abs/2105.09063v1
• Date: Wed, 19 May 2021 11:12:09 GMT
• Title: Deep Learning Radio Frequency Signal Classification with Hybrid Images
• Authors: Hilal Elyousseph, Majid L Altamimi
• Abstract summary: We focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed Deep Learning architecture. We propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our initial results point out limitations to classical pre-processing approaches while also showing that it's possible to build a classifier that can leverage the strengths of multiple signal representations.

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