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Traffic Burst Prediction in Radio Access Network with Machine Learning
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Motivated by the expansion of mobile data traffic, there is an increasingdemand for better allocation of radio resources in the radio access network(RAN). Recently, interest has shifted towards predictive resource allocationtechniques, which would enable a more intelligent RAN. A promising solutionfor developing predictive resource allocation techniques is to combine radioresource allocation algorithms with prediction algorithms based on Machinelearning (ML). In this project, the prediction of data traffic in RAN withML techniques is studied, with the objective to incorporate the predictor incarrier aggregation. The traffic predicted in this project is at the burst levelwhich is an aggregation of several consecutive packets, and the focus is onsupervised classification algorithms. The volume of the burst, burst durationtime and the time gap between two bursts are predicted. The performanceof prediction is evaluated with the receiver operating characteristic curve.

Abstract [sv]

Motiverad av ökningen i mobil datatrafik, det finns en ökande efterfråganpå bättre fördelning av radioresurser i radioaccessnät (RAN). Nyligenhar intresset förskjutits mot prediktiva resursfördelnings tekniker, som skullemöjliggöra en mer intelligent RAN. En lovande lösning för att utvecklaprediktiva resursfördelning tekniker är att kombinera resurstilldelningsalgoritmermed förutsägelsealgoritmer baserade på maskininlärning (ML). I dettaprojekt studeras förutsägelsen av datatrafik i RAN med ML tekniker, i syfteatt använda prediktorn för bäraraggregering. Trafiken som betraktas i dettaprojekt är på skur nivå som är en sammanslagning av flera på varandraföljande paket, och fokus ligger på övervakade klassificeringsalgoritmer. Detär volymen av skuren, längden, och tidsskillnaden mellan två skurar somförutsägs. Prestandan hos förutsägelse utvärderas med receiver operatingcharacteristic kurvan.

Place, publisher, year, edition, pages
2016.
Series
TRITA-EE, ISSN 1653-5146 ; 2016:174
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-197206OAI: oai:DiVA.org:kth-197206DiVA, id: diva2:1050774
External cooperation
Ericsson Research
Supervisors
Available from: 2017-02-08 Created: 2016-11-30 Last updated: 2017-02-08Bibliographically approved

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CiteExportLink to record
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