Intelligent Dynamic Spectrum Access Assisted by Channel Usage Prediction

To solve the scarcity of wireless spectrum, cognitive radio (CR) is proposed to let unlicensed wireless users (secondary users) dynamically sense and access unused channels without generating interference to licensed users (primary users). The performance of CR-based dynamic spectrum access (DSA) mechanism can be dramatically improved if the wireless spectrum usage is predictable, and many works have been conducted based on the assumption that wireless spectrum usage can be predicted by Markov model based methods. To verify and study the predictability of real world wireless spectrum usage, we study the result of a large scale measurement and find that the wireless spectrum is non-stationary, which means its background probabilistic model is varying all the time, therefore the error of prediction is not avoidable. Therefore, to improve the performance of DSA by leveraging spectrum usage prediction, the prediction error must be considered. In this paper, we study the error of prediction, and figure out that the distribution of prediction error can be well approximated by Beta Distribution. We then design a prediction error-aware dynamic spectrum access scheme. The study result shows that it outperforms those prediction-based DSA schemes which are not aware of prediction errors.