A master thesis from Aalborg University
Mathematical Modelling and Prediction of Interference Power in In-robot Subnetworks
Author(s)
Term
4. semester
Education
Publication year
2022
Submitted on
2022-06-02
Pages
92 pages
Abstract
The envisioned wireless networks, 6G in-X subnetworks have extreme requirements for latency, data rate and reliability. The in-X subnetworks considered in this thesis, is the in-robot subnetworks in a factory setting. The interference power which comes from the in-robot subnetworks in a factory setting is analyzed and predicted throughout this thesis. Analytical expressions for the mean and auto-correlation function (ACF) of the interference power are derived. The expressions for the mean and ACF contained high dimensional integrals which we estimate using Monte Carlo (MC) integration. When estimating the expression for the mean it was found that the analytical mean fitted the mean of the interference power obtained from simulations. It was found that when estimating the expression for the ACF using MC integration that it would overestimate the ACF of simulated interference power. However, the ACFs had the same form, thus, an appropriate scaling could make them coincide. Furthermore, the interference power simulated from in-robot subnetworks was predicted using an autoregressive (AR) model of order 20. We also found that the interference power could be predicted using the AR(20) predictor for up to 8 [ms] when the velocity is 2 [m/s]. Additionally, the AR(20) predictor outperformed the last value predictor for all settings.
Keywords
Documents
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