Prediction of Channel Availability in Cognitive Radio Networks Using a Logistic Regression Algorithm

Abstract—The capacity of predicting spectral occupancy in cognitive radio networks offers the possibility of developing better policies in channel assignment to secondary users, according to the predicted spectral opportunities. This work develops a prediction model to determine and exploit spectral opportunities while avoiding the continuous search for channel availability in cognitive radio networks. The proposed scheme creates an availability prediction matrix for every available channel in the GSM band that includes their times of availability. By using this information, there is a potential to improve channel allocation policies. The model contains two processes: the first one performs a training process in order to prepare the prediction algorithm so that it can make more reliable predictions and the second one uses the logistic regression algorithm to estimate the availability in every available frequency which can be profited by secondary users, who intend to start transmissions. Measurements were made for average bandwidth, average delay and prediction error. The results obtained were evaluated with real spectral occupancy data in the GSM frequency band. The developed model shows a low prediction error which enables optimal channel assignment mechanisms, hence minimizing failed handoffs through the channel occupation of primary users. Keyword Availability, Cognitive radio, Logistic regression, Prediction.