Linear Algorithms for Radioelectric Spectrum Forecast
AbstractThis paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables. View Full-Text
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Pedraza, L.F.; Hernandez, C.A.; Paez, I.P.; Ortiz, J.E.; Rodriguez-Colina, E. Linear Algorithms for Radioelectric Spectrum Forecast. Algorithms 2016, 9, 82.
Pedraza LF, Hernandez CA, Paez IP, Ortiz JE, Rodriguez-Colina E. Linear Algorithms for Radioelectric Spectrum Forecast. Algorithms. 2016; 9(4):82.Chicago/Turabian Style
Pedraza, Luis F.; Hernandez, Cesar A.; Paez, Ingrid P.; Ortiz, Jorge E.; Rodriguez-Colina, E. 2016. "Linear Algorithms for Radioelectric Spectrum Forecast." Algorithms 9, no. 4: 82.
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