Spectrum prediction has recently gained a lot of attention due to its extensive applications in cognitive radio networks. However, most of the related research assumed that the spectrum occupancy pattern is time-invariant, which limits the performance of proposed prediction methods for the high burst frequency bands, e.g. the ISM (Industrial, Scientific, Medical) bands. In order to improve the prediction accuracy for them, this paper first analyzes the characteristics of the collected real WiFi data in 2.4GHz ISM band, and shows the burstiness of the band from multiple aspects. Based on the analysis, we then propose a MultiLayer Perceptron based Reinforcement Learning (RLMLP) method which could adaptively select the corresponding predicting action according to the state to which any piece of data belongs. The state space of the method consists of multiple data categories that are determined by the results of feature partitioning, while the action space is composed of multiple MLPs with the same mapping structure. Finally, numerical results on the collected data show that the proposed RLMLP method is significantly better than the state-of-the-art algorithms in terms of the prediction performance.
Spectrum Prediction for Frequency Bands with High Burstiness: Analysis and Method
2020-05-01
309834 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch