Spectrum prediction based on GAN and deep transfer learning: A cross-band data augmentation framework

This paper investigates the problem of data scarcity in spectrum prediction. A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes. The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency. Moreover, the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited. To address the above issues, we develop a cross-band data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network (GAN) and deep transfer learning. Firstly, through the similarity measurement, we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band. Then, through the data augmentation by feeding the small amount of the target data into the pre-trained GAN, temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN. Finally, experiment results demonstrate the effectiveness of the proposed framework.