Long-Term Spectrum State Prediction: An Image Inference Perspective

• Main Authors: Jiachen Sun, Jinlong Wang, Guoru Ding, Liang Shen, Jian Yang, Qihui Wu, Ling Yu
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Spectrum prediction; image inference; tensor completion; cognitive radio
• Online Access: https://ieeexplore.ieee.org/document/8423633/

Spectrum prediction techniques have drawn much attention for enabling the dynamic spectrum access. As new algorithms emerge endlessly, most of them can only predict the future spectrum states in a slot-by-slot manner. A new thought to realize the long-term and comprehensive spectrum state prediction efficiently is deserving our exploration. In this paper, we formulate the spectrum situation of multiple frequency points or bands in a whole day with multiple time slots as an “image”and propose an idea of image inference to predict the spectrum situation of a whole day in the future based on multiple “images”composed of historical spectrum data. First, we model a new kind of three-order spectrum tensor and convert the spectrum prediction problem to a tensor completion problem. We analyze the impacts of prefilling proportion and the parameter m of the third dimension on the prediction performance via an illustrative example of predicting a mosaic image. Then, a new long-term spectrum prediction scheme based on tensor completion (LSP-TC) is developed. Experiments with real-world satellite spectrum data demonstrates that the proposed LSP-TC is superior to the benchmark scheme in both the accuracy and the runtime overhead of prediction.