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Open AccessArticle

Linear Algorithms for Radioelectric Spectrum Forecast

Faculty of Technology, Universidad Distrital Francisco José de Caldas, Bogota 110231, Colombia
Industrial and Systems Engineering Department, Faculty of Engineering, Universidad Nacional de Colombia, Bogota 111321, Colombia
Electrical Engineering Department, Universidad Autónoma Metropolitana Iztapalapa, Mexico City 09340, Mexico
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2016, 9(4), 82;
Received: 2 August 2016 / Revised: 25 October 2016 / Accepted: 28 November 2016 / Published: 2 December 2016
PDF [5765 KB, uploaded 2 December 2016]


This 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
Keywords: cognitive radio; time series; linear algorithms cognitive radio; time series; linear algorithms

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MDPI and ACS Style

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.

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