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 process-ing.Nowadays,deep learning has become a key research component of the Sixth-Generation wire-less 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.

Ya TU;Yun LIN;Haoran ZHA;Ju ZHANG;Yu WANG;Guan GUI;Shiwen MAO

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

中国航空学报（英文版）

[1]Ya TU;Yun LIN;Haoran ZHA;Ju ZHANG;Yu WANG;Guan GUI;Shiwen MAO-.Large-scale real-world radio signal recognition with deep learning)[J].中国航空学报（英文版）,2022(09):35-48

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