Hybrid Clustering and Deep Learning-based Spatio Temporal Analysis of Spectrum Utilization
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Date
2021-10
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Addis Ababa University
Abstract
Radio spectrum is a finite resource, while the demand for wireless systems is increasing
at an exponential rate. To meet this demand, new generations of cellular networks were
introduced.Spectrum utilization of cellular bands is analyzed widely using spectrum measurements.
Knowledge of spectrum utilization will help operators like Ethio telecom to
understand and plan band usage.
In this thesis, using the K-means algorithm and Deep learning algorithms, namely Convolutional
Neural Network (CNN) and Long Short Term Memory (LSTM), downlink Global
System for Mobile Communication (GSM) 900 spectrum utilization is analyzed and modeled
to know the spectrum utilization of Ethio telecom. The data is collected from Addis
Ababa 639 GSM base stations. Spectrum utilization is modeled using CNN and LSTM
algorithms for clustered and non-clustered data. Because of the differences in base station
behavior, clustering base stations is done and model the spectrum utilization of the base
stations in each cluster.
Our results show that the GSM 900 downlink spectrum is not utilized optimally. The
highest observed average spectrum utilization was 71%, with the lowest observed average
spectrum utilization being 1.4%. The model developed for the cluster data using the CNN
algorithm can model spectrum utilization with an RMSE value of 0.58 and this model can
predict the next twenty-four-hour base station spectrum utilization with an RMSE value
of 1.04.
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Keywords
Spectrum Utilization, GSM900, Downlink, K-means, LSTM, CNN