Channel Availability Prediction in Cognitive Radio Networks Using Naive Bayes

This work develops a channel availability predictive model that allows taking advantage of opportunities in the cognitive radio network’s spectrum in the most efficient way. The developed scheme creates an availability prediction matrix for each available channel in the GSM band, which is used to determine the potential bandwidth and availability time slots to optimize the channel allocation policies. The allocation model contains a training process in charge of preparing the prediction algorithm so it can make predictions that are more reliable. Additionally, the prediction procedure uses the Naïve Bayes algorithm to estimate the availability in each available frequency channel. This facilitates broadcasting for secondary users. Measurements were computed for the average bandwidth, the average delay and the prediction error. The obtained results were assessed with real spectral occupation data in the GSM frequency band. The developed model shows a low prediction error, which leads to establishing optimal channel allocation mechanisms hence minimizing the failed handoffs due to channel occupation by primary users.