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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-354686 |
| Date | January 2018 |
| Creators | Wännström, Linus |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 18032 |
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