DeepAir: Enabling Data-Driven Dynamic Spectrum Sharing via Scalable Forecasting

The rapid uptake of wireless technologies over the past decade has resulted in an increasing pressure on the limited radio spectrum resources. To improve the efficiency of current allocation policies, regulators in many jurisdictions are considering dynamic spectrum sharing. The success, however, of an optimized system hinges on the ability to sense, characterize, and forecast spectrum usage behaviour. Since traditional methods prove unable to scale to a wide range of channels, we propose DeepAir, a robust and scalable model that is capable of learning and predicting complex temporal and spectral dependencies in multivariate spectrum data. Specifically, we design a Sequence-to-Sequence model that employs an encoder-decoder architecture with two Deep Temporal Convolutional Networks. Using a test set consisting of approximately 900 channels in the Land Mobile Radio bands, we obtain a median RMSE and median MAE of 6.51 and 5.15, respectively. We then apply transfer learning to demonstrate the effectiveness of this model in forecasting patterns from any sensor, regardless of the band, sensitivity, and geographical location. Furthermore, the model exhibits no performance degradation up to three years after training for both short and long forecast horizons. Finally, we use DeepAir to quantify spectrum availability to enhance existing spectrum sharing capabilities.