Cross-band spectrum prediction algorithm based on data conversion using generative adversarial networks

Abstract

Spectrum prediction is one of the new techniques in cognitive radio that predicts changes in the spectrum state and plays a crucial role in improving spectrum sensing performance. Prediction models previously trained in the source band tend to perform poorly in the new target band because of changes in the channel. In addition, cognitive radio devices require dynamic spectrum access, which means that the time to retrain the model in the new band is minimal. To increase the amount of data in the target band, we use the GAN to convert the data of source band into target band. First, we analyze the data differences between bands and calculate FID scores to identify the available bands with the slightest difference from the target predicted band. The original GAN structure is unsuitable for converting spectrum data, and we propose the spectrum data conversion GAN (SDC-GAN). The generator module consists of a convolutional network and an LSTM module that can integrate multiple features of the data and can convert data from the source band to the target band. Finally, we use the generated target band data to train the prediction model. The experimental results validate the effectiveness of the proposed algorithm.