Abstract
Determining the state of each cell, for instance, cell outages, in a densely deployed cellular network is a difficult problem. Several prior studies have used minimization of drive test (MDT) reports to detect cell outages. In this paper, we propose a two step process. First, using the MDT reports, we estimate the serving base station’s transmit power for each user. Second, we learn summary statistics of estimated transmit power for various networks states and use these to classify the network state on test data. Our approach is able to achieve an accuracy of 96% on an NS-3 simulation dataset. Decision tree, random forest and SVM classifiers were able to achieve a classification accuracy of 72.3%, 76.52% and 77.48%, respectively .
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Under obstructions or irregular terrains, this would not hold for many users. Nevertheless, several users may still be located such that this condition holds.
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Asghar, M.Z. et al. (2019). Cell State Prediction Through Distributed Estimation of Transmit Power. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2019 2019. Lecture Notes in Computer Science(), vol 11660. Springer, Cham. https://doi.org/10.1007/978-3-030-30859-9_31
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DOI: https://doi.org/10.1007/978-3-030-30859-9_31
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