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.