  Radio signal classification has a very wide range of applications in the
field of wireless communications and electromagnetic spectrum management. In
recent years, deep learning has been used to solve the problem of radio signal
classification and has achieved good results. However, the radio signal data
currently used is very limited in scale. In order to verify the performance of
the deep learning-based radio signal classification on real-world radio signal
data, in this paper we conduct experiments on large-scale real-world ACARS and
ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively,
and with categories of 3,143 and 5,157 respectively. We use the same
Inception-Residual neural network model structure for ACARS signal
classification and ADS-B signal classification to verify the ability of a
single basic deep neural network model structure to process different types of
radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B.
We build an experimental system for radio signal deep learning experiments.
Experimental results show that the signal classification accuracy of ACARS and
ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with
injected additive white Gaussian noise) is greater than 9 dB, the
classification accuracy is greater than 92%. These experimental results
validate the ability of deep learning to classify large-scale real-world radio
signals. The results of the transfer learning experiment show that the model
trained on large-scale ADS-B datasets is more conducive to the learning and
training of new tasks than the model trained on small-scale datasets.
