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Prediction of Inter-Frequency Measurements in a LTE Network with Deep Learning
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Prediktering av inter-frekvensmätningar i ett LTE-nätverk med Deep Learning (Swedish)
Abstract [en]

The telecommunications industry faces difficult challenges as more and more devices communicate over the internet. A telecommunications network is a complex system with many parts and some are candidates for further automation. We have focused on interfrequency measurements that are used during inter-frequency handovers, among other procedures. A handover is the procedure when for instance a phone changes the base station it communicates with and the inter-frequency measurements are rather expensive to perform.

More specifically, we have investigated the possibility of using deep learning—an ever expanding field in machine learning—for predicting inter-frequency measurements in a Long Term Evolution (LTE) network. We have focused on the multi-layer perceptron and extended it with (variational) autoencoders or modified it through dropout such that it approximate the predictive distribution of a Gaussian process.

The telecommunications network consist of many cells and each cell gather its own data. One of the strengths of deep learning models is that they usually increase their performance as more and more data is used. We have investigated whether we do see an increase in performance if we combine data from multiple cells and the results show that this is not necessarily the case. The performances are comparable between models trained on combined data from multiple cells and models trained on data from individual cells. We can expect the multi-layer perceptron to perform better than a linear regression model.

The best performing multi-layer perceptron architectures have been rather shallow, 1-2 hidden layers, and the extensions/modifications we have used/done have not shown any significant improvements to warrant their presence.

For the particular LTE network we have worked with we would recommend to use shallow multi-layer perceptron architectures as far as deep learning models are concerned.

Place, publisher, year, edition, pages
2018. , p. 48
Keywords [en]
Telecommunications, Mobile Networks, 4G, LTE, Handover, Load Balancing, Machine Learning, Deep Learning, Neural Networks, Supervised Learning, Autoencoder
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-151879ISRN: LIU-IDA/STAT-A–18/005–SEOAI: oai:DiVA.org:liu-151879DiVA, id: diva2:1253921
Subject / course
Statistics
Supervisors
Examiners
Available from: 2018-10-24 Created: 2018-10-07 Last updated: 2018-10-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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