Digital Twin Based Network Latency Prediction in Vehicular Networks
Journal Publication ResearchOnline@JCUAbstract
Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN.
Journal
Electronics
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Volume
11
ISBN/ISSN
2079-9292
Edition
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Issue
14
Pages Count
21
Location
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Publisher
Molecular Diversity Preservation International (MDPI)
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EISSN
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DOI
10.3390/electronics11142217