Modeling GSM Spectrum Occupancy Using Time Series Analysis: The case of Ethio telecom

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Addis Ababa University


Spectrum occupancy models can help you make better use of the radio spectrum. It has also been extensively researched in recent decades as it is critical for developing new regulations for spectrum allocation for future technologies as well as monitoring the activities that take place on that spectrum. Understanding the amount of available spectrum is critical for future wireless technologies that want to address the so-called spectrum scarcity issue. Spectrum occupancy measurements provide critical data for frequency planning and optimization, as well as assist in smart decision-making. The goal of time series modeling is to collect and thoroughly examine previous data from a time series in order to construct an appropriate model that accurately captures the series’ intrinsic structure. This thesis examines three types of time series analysis methods: Holt-winters, Seasonal Auto Regressive Integrated Moving Average (SARIMA), and SARIMA eXogenous regresses (SARIMAX) based models, as well as their inherent prediction strengths and weaknesses. Time series modeling principles such as trend, stationarity, seasonality, residual, and so on have also been covered. To assess the accuracy rate, we fitted multiple models to a time series using five primary metrics. Among the methods used are mean square error, mean absolute error (MAE), root-mean-square error, mean absolute percentage error, and R-squared. At 1800MHz, the maximum spectrum occupancy is 60.35%, and at 900MHz, it is 44.71%. For 900MHz MAE, the SARIMAX model produced better predictions (35.34% and 50.1% lower than the SARIMA and Holt-Winter models, respectively), while for 1800MHz, the SARIMAX model produced 42.6% and 52.6% lower than the SARIMA and Holt-Winter models, respectively



Spectrum Occupancy, Holt-Winters, SARIMA, SARIMAX