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This contribution aims at experimentally validating the suitability of Gaussian mixture (GM) distributions to capture the stochastic characteristics of outdoor terahertz (THz) wireless channels. In this direction, we employ a machine learning enabled approach, based on the expectation maximization algorithm, in order to identify the suitable number of Gaussian distributions as well as their corresponding parameters that result to an acceptable fit. The fitting accuracy of the GMs to the empirical distributions is evaluated by means of the Kolmogorov-Smirnov (KS), Kullback-Leibler (KL), root-mean-square-error (RMSE) and R2 fitting accuracy tests. These tests verify the suitability of GMs to model the small-scale fading channel amplitude of outdoor THz wireless links. In addition, the fitting accuracy results indicate that as the number of mixtures increases the resulting GMs achieve a better fit to the empirical data.
|Title of host publication||2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022|
|Number of pages||6|
|Publication status||Published - 2022|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Virtual, Online, Japan|
Duration: 12 Sep 2022 → 15 Sep 2022
|Name||IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications|
|Conference||IEEE International Symposium on Personal, Indoor and Mobile Radio Communications|
|Period||12/09/2022 → 15/09/2022|
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- 1 Active
ARIADNE: Artificial Intelligence Aided D-band Network for 5G Long Term Evolution
Tretiakov, S., Tcvetkova, S. & Kosulnikov, S.
01/11/2019 → 31/07/2023
Project: EU: Framework programmes funding