Abstract
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.
Highlights
Radioelectric spectrum occupancy is widely studied due to its importance for the construction of new spectrum assigning policies in emerging technologies, as well as in monitoring activities both in licensed and unlicensed bands
The deviation was observed; the generalized autoregressive conditional heteroskedasticity (GARCH) algorithm was used to forecast the behavior of measured main difference among former models and GARCH model lies in the constant variance assumption
The seasonal autoregressive integrated moving average (SARIMA) and GARCH algorithms have been evaluated in this paper in order to forecast the reception power in channels of a global systems for mobile communications (GSM) band
Summary
Radioelectric spectrum occupancy is widely studied due to its importance for the construction of new spectrum assigning policies in emerging technologies, as well as in monitoring activities both in licensed and unlicensed bands. Precise parameter estimates like time quantity and geographical region where the different spectrum band is used bring useful information to determine spectral opportunities for variant technologies within a domain. In this paper, such technologies correspond to a global systems for mobile communications (GSM) technology variant in the time domain [2]. The spectrum sensing in cognitive radio (CR) provides the necessary information about the status of the wireless channels, modeling and prediction of communications activity This could contribute to spectral efficiency improvement efforts [3,4,5]. Based on prediction information, SUs can select the channels with higher probability of vacancy in multi-channel wideband sensing scenarios [7], and
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