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
A machine learning based approach for the link-to-system mapping problem
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The quality of mobile communication is related to signal transmissions. Early detection of the errors in transmissions may reduce the time delay of communications. The traditional error detection methods are not accurate enough. Therefore, in this report, a machine learning based approach is proposed for the link-to-system mapping problem, which can predict the outcomes (received correctly or not) of the link-level simulations without knowing the exact signals that are being transmitted. In this method, the transmission state is assumed to be a function of the features of a channel environment like the interference and the noise, the relative motion between the transmitter and the receiver and this function is obtained using a machine learning method. The training dataset is generated by simulations of the channel environment. Logistic regression, support vector machine and neural networks are the three algorithms implemented in this thesis. Experimental results show that all three algorithms work well compared to traditional methods. Neural networks provide the best results for this problem. Furthermore, the neural network model is tested with a dataset consisting of features of ten different channel environments, which verified the generalization ability of the model.

Place, publisher, year, edition, pages
2017. , 48 p.
Keyword [en]
Machine learning, wireless communication, Link to system mapping
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-214852OAI: oai:DiVA.org:kth-214852DiVA: diva2:1143919
External cooperation
Huawei Technologies Sweden AB
Educational program
Master of Science - Machine Learning
Presentation
2017-06-27, room 304 (22:an), Teknikringen 14, Stockholm, 15:00 (English)
Supervisors
Examiners
Available from: 2017-10-27 Created: 2017-09-23 Last updated: 2017-10-27Bibliographically approved

Open Access in DiVA

fulltext(2405 kB)5 downloads
File information
File name FULLTEXT01.pdfFile size 2405 kBChecksum SHA-512
d9e53556604b50ba9aa23ecdbe5793695aff04137088fe7eb0a54383d0a941387849491367738985a91118275e753f74182ebe442a14b4f7aa8bf9b4b6d8a52e
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 5 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

Total: 24 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