Deep Learning Models for Spectrum Prediction: A Review

Spectrum prediction is a promising technique for improving spectrum exploitation in cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the energy consumption of spectrum sensing and improving the network throughput, etc. Recently, a significant amount of research efforts has been devoted to this area, especially deep learning (DL) methods, and greatly advanced spectrum prediction abilities. This article reviews the state-of-the-art developments in DL for spectrum prediction. Specifically, we first summarize the existing spectrum prediction methods and give a taxonomy from different aspects. Second, we formulate long-term, multidimensional, and nonideal spectrum prediction problems to provide theoretical support for spectrum prediction models. Third, we divide the DL models into four categories: basic models, models for the long-term domain, models for the multidimensional domain, and models for the nonideal domain based on application scenarios and offer an in-depth tutorial on these models. Fourth, we comprehensively collect and organize widely used common spectrum data sources in the existing literature and also overview the spectrum data simulation methods to facilitate other researchers. Furthermore, we summarize and analyze the selection of evaluation metrics for spectrum prediction models based on DL. Finally, we summarize the research trends and highlight the critical research challenges.