Digital Twin for Latency Prediction in Communication Networks
Authors
Term
4. Semester
Education
Publication year
2023
Submitted on
2023-05-31
Pages
84
Abstract
This report investigates the problem of latency in a wireless network scenario, and proposes the idea of using Neural Network models in a Digital Twin in order to predict the latency in real-time. Different Digital Twin structures were proposed, including different amount of Neural Network models as well as different inputs. To accompany the Digital Twin, a Physical Twin with a client-server file transmission use case was developed, in order to obtain values of latency. Based on data available before a transmission, such as the physical location of the server and file size used, the Digital Twin was trained to predict. It was found that the Digital Twin was capable of making predictions in under a millisecond by implementing the Neural Network model as TensorFLow Lite models. This was significantly faster than Physical Twin in all scenarios, including when the observed latency was the lowest. The latency prediction itself was successful, and a number of future considerations for more accurate predictions were proposed. These considerations include how to accommodate for temporal characteristics in the observed latency.
Keywords
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