Large-scale real-world radio signal recognition with deep learning |
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Affiliation: | College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;College of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China;College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Department of Electrical and Computer Engineering,Auburn University,Auburn 36849,USA |
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Abstract: | In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area. |
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Keywords: | Signal recognition Radio signal dataset Automatic Dependent Surveillance-Broadcast (ADS-B) Deep learning Recognition benchmark |
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