TF 2 AN: A Temporal-Frequency Fusion Attention Network for Spectrum Energy Level Prediction

Modeling and predicting radio spectrum are significant for better understanding the behavior of spectrum, managing their usage as well as optimizing the performance of dynamic spectrum access. Most of the existing works concentrate on predicting the occupation status of the spectrum via threshold-based binary time series, ignoring abundant frequency correlations. In fact, precisely predicting the energy level of the radio spectrum can provide richer information for applications such as characterizing the spectrum trending for earlier anomaly detection and estimating the channel quality for efficient spectrum sharing. However, the precise prediction is challenging due to the interference from both intra-spectrum and external factors. In this paper, we propose a temporal-frequency fusion attention network to model the complex internal and external correlations for precise prediction. More specifically, our framework consists of three major components: 1) an image processing based robust signal detection algorithm to locate the signal as model input. 2) an attention-based Long Short-term Memory network to model the temporal-frequency correlation of the spectrum. 3) a generalized fusion module to take in the external factors from heterogeneous domains. Extensive experiments are conducted on real-world datasets collected by our spectrum monitoring station deployed in the city of Hangzhou, China, which shows that the proposed signal detection algorithm is robust for frequency bands with different signal to noise ratios. Furthermore, experimental results demonstrate that our method outperforms seven baseline methods in terms of prediction accuracy. The sensitivities of hyper-parameters are analyzed and the interpretability is also well discussed to prove the effectiveness of our method.