Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analysis of Wi-Fi performance data for a Wi-Fi throughput prediction approach
KTH, School of Information and Communication Technology (ICT).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Due to low cost and portability of Wi-Fi technologies, wireless network deployment has been widely accepted in the residential environment. The evaluation results of customers’ home wireless network performance level provides a reference for operators to improve their network capacity in order to face the emerging requirement of Wi-Fi service. However, the dynamic nature of Wi-Fi network makes Wi-Fi performance analysis difficult to perform.

In this thesis, a Wi-Fi parameter visualization tool is implemented to show users’ Wi-Fi performance in a graphic way. This tool could help operators investigate customers’ Wi-Fi environment to see if performance degradation exists or not. Besides, a machine learning method is used for Wi-Fi performance analysis to predict Wi-Fi throughput. A SVM-based classification model is proposed to work as a prediction function.

This function takes Wi-Fi parameters both for target AP and nearby interference APs as input, and output is categorized Wi-Fi throughput, good, medium, poor or very poor. Different SVM kernel functions conducted to evaluate the proposed model and results show that classification accuracy can be up to 0.88. It demonstrates that Wi-Fi throughput could be classified using a simple measurement way and limited Wi-Fi physical parameters.

Abstract [sv]

På grund av låg kostnad och hög bärbarhet, för Wi-Fi-teknik, har trådlösa nätverk blivit mycket vanliga i bostadsmiljön. Den stora anvndningen av Wi-Fi-tjänster betyder att operatrerna vill förbättra nätverkstjänsterna, genom att känna till kundernas prestanda fr deras trådlsa nätverk i hemmen. De dynamiska egenskaperna hos Wi-Fi-ntverk gr det dock svårt att utföra analysen av Wi-Fi data.

I denna avhandling implementeras ett Wi-Fi-parameter visualiseringsverktyg, för att visa användarnas Wi-Fi-prestanda på ett graskt stt. Det här verktyget kan hjälpa operatörer att underska kundernas Wi-Fi-miljö, för att se om prestanda försämras eller ej.

Dessutom föreslås en SVM-baserad klassiceringsmodell för att förutsäga Wi-Fi-genomstrmning. Denna klassiceringsmodell fungerar som en prediktionsfunktion som tar Wi-Fi-parametrar både för den egna accesspunkten och närliggande accesspunkters interferens som input, och för utsignalen kategoriseras datatakten som: bra, medium, fattig eller mycket dålig. Olika SVM-körfunktioner utförda för att utvärdera den föreslagna modellen och resultaten visar att klassiceringsnoggrannheten kan vara upp till 0,88. Det visar att Wi-Fi-datatakten kan klassiceras med ett enkelt mätverktyg och genom att känna till begränsat antal Wi-Fiparametrar.

Place, publisher, year, edition, pages
2017. , p. 39
Series
TRITA-ICT-EX ; 2017:63
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-215690OAI: oai:DiVA.org:kth-215690DiVA, id: diva2:1148996
External cooperation
Telenor
Subject / course
Electrical Engineering
Educational program
Master of Science -Communication Systems
Supervisors
Examiners
Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2017-10-20Bibliographically approved

Open Access in DiVA

fulltext(6348 kB)643 downloads
File information
File name FULLTEXT01.pdfFile size 6348 kBChecksum SHA-512
f611167032b2d499f945a8f74c81aada6a00b4c7ea4bcde3b887a1ce9a47be6e44bb850ed38a407e5ba0ec708407270343a7e87d8226af5c467e5e5452fc1f4f
Type fulltextMimetype application/pdf

By organisation
School of Information and Communication Technology (ICT)
Electrical Engineering, Electronic Engineering, Information EngineeringCommunication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 643 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 122 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf