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Assisted Partial Timing Support Using Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Assisted partial timing support is a method to enhance the synchronization of communication networks based on the Precision Timing Protocol. One of the main benefits of the Precision Timing Protocol is that it can utilize a method called holdover through which synchronization in communication networks can be maintained, however, holdover is easily impacted by network load which may cause it to deviate from a microsecond accuracy that is required.

In this project, neural networks are investigated as an aid to assisted partial timing support with the intention to combat the effects of network load. This hypothesis is to achieve this through a neural network being able to predict the offset due to time delay in the communication networks and thus being able to cancel out this effect from previous offset. Feed-forward and recurrent neural networks are tested on four different types of load patterns that commonly occur on communication networks.

The results show that although some level of prediction is possible, the accuracy with which the tested neural networks provide prediction is not high enough to allow it to be used for compensation of the offset caused by the load. This with the best result reaching a mean squared error of ten microseconds squared and the requirement looked for was for where the maximum was one microsecond. This project only looked at short periods of the load patterns and future areas to investigate could be looking at longer periods of the load patterns.

Place, publisher, year, edition, pages
2018. , p. 31
Series
UPTEC F, ISSN 1401-5757 ; 18032
Keywords [en]
neural networks, time synchronization, precision time protocol
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:uu:diva-354686OAI: oai:DiVA.org:uu-354686DiVA, id: diva2:1222245
External cooperation
Ericsson AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-21 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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