LAUSR

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data. In practice, for a given spectrum band of interest, when facing relatively scarce historical data, spectrum prediction based on traditional learning methods does not work well. Thus, this paper proposes a cross-band spectrum prediction model based on transfer learning. Firstly, by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping, the similarity between spectrum bands has been verified. Next, the features, which mainly affect the performance of transfer learning in the crossband spectrum prediction, are explored by leveraging transfer component analysis. Then, the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated. Further, experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-the-art models when the historical spectrum data is limited.