A Spectrum Prediction Method for Bursty Frequency Bands
Chao Yang, Tao Peng, Peiliang Zuo, Xinyue Wang · 2021
Spectrum prediction is an important technology for spectrum cognition which constitutes the premise of cognitive radio technology. However, most of the existing spectrum prediction methods were proposed for common frequency bands. Based on this philosophy, these methods generally build a mapping model between historical statistical information and future state, and performance of the single model was verified to be limited due to that it cannot match the high burstiness of spectrum. This paper considers the prediction of bursty bands. The previous collected data of 2.4GHz Industrial, Scientific, Medical (ISM) bands is utilized for both feature analysis and method research. Specifically, the deep-reinforcement learning (DRL) method is adopted to address the high-dimensional state and action spaces caused by the classification process using the values of several features. Besides, this paper also employs the upper-limit results of the prediction to refine the classification process, i.e. those groups with better prediction performance using predictors corresponding to other classes will be readjusted. We thus name the proposed method as classification-based deep reinforcement learning (C-DRL). Finally, extensive experiments with the collected real data are conducted to verify the performance of C-DRL. The results suggest that C-DRL significantly outperforms the state-of-the-art algorithms in terms of the prediction performance.