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Predicting inter-frequency measurements in an LTE network using supervised machine learning: a comparative study of learning algorithms and data processing techniques
Linköping University, Department of Computer and Information Science.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att prediktera inter-frekvensmätningar i ett LTE-nätverk med hjälp av övervakad maskininlärning (Swedish)
Abstract [en]

With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predictive performance on this data set, at 90.7\% accuracy. In addition, we have shown that undersampling and varying the discriminator are effective techniques for increasing the performance on the positive class on frequencies where the negative class is prevalent. Finally, we present hybrid algorithms in which the learning algorithm for each model depends on attributes of the training data set. These algorithms perform at a much higher efficiency in terms of memory and run-time without heavily sacrificing predictive performance.

Place, publisher, year, edition, pages
2018. , p. 52
Keywords [en]
Telecommunications, Telecom, Mobile networks, 4G, LTE, LTE-A, Machine learning, Random forest, Gradient boosting, Neural network, Multi-layer perceptron, Logistic regression, Frequency measurements, Handover, Load balancing, Carrier aggregation
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-148553ISRN: LIU-IDA/LITH-EX-A--18/017--SEOAI: oai:DiVA.org:liu-148553DiVA, id: diva2:1217651
External cooperation
Ericsson AB
Subject / course
Computer science
Presentation
2018-06-05, Herbert Simon, Linköping, 16:15 (Swedish)
Supervisors
Examiners
Available from: 2018-07-02 Created: 2018-06-13 Last updated: 2018-07-02Bibliographically approved

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fulltext(2002 kB)38 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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