Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes

Evangelos N. Papasotiriou; Alexandros Apostolos A. Boulogeorgos; Mar Francis De Guzman; Katsuyuki Haneda; Angeliki Alexiou

doi:10.1109/PIMRC54779.2022.9977784

Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes

Evangelos N. Papasotiriou^*, Alexandros Apostolos A. Boulogeorgos, Mar Francis De Guzman, Katsuyuki Haneda, Angeliki Alexiou

^*Tämän työn vastaava kirjoittaja

University of Piraeus

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

2 Sitaatiot (Scopus)

98 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	978-1-6654-8053-6
DOI - pysyväislinkit	https://doi.org/10.1109/PIMRC54779.2022.9977784
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Virtual, Online, Japani Kesto: 12 syysk. 2022 → 15 syysk. 2022

Julkaisusarja

Nimi	IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications
ISSN (painettu)	2166-9570
ISSN (elektroninen)	2166-9589

Conference

Conference	IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Lyhennettä	PIMRC
Maa/Alue	Japani
Kaupunki	Virtual, Online
Ajanjakso	12/09/2022 → 15/09/2022

Pääsy asiakirjaan

10.1109/PIMRC54779.2022.9977784

Stochastic_Characterization_of_outdoor_Terahertz_Channels_Through_Mixture_Gaussian_ProcessesFinal published version, 1,99 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

ARtificial Intelligence Aided D-band Network for 5G long term Evolution
Tretiakov, S. (Vastuullinen tutkija)
01/01/2020 → 31/10/2022
Projekti: EU H2020 Framework program

Aalto Electronics-ICT
Ryynänen, J. (Manager)
Elektroniikan ja nanotekniikan laitos
Laitteistot/tilat: Facility

Siteeraa tätä

Papasotiriou, E. N., Boulogeorgos, A. A. A., De Guzman, M. F., Haneda, K., & Alexiou, A. (2022). Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes. teoksessa 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022 (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications). IEEE. https://doi.org/10.1109/PIMRC54779.2022.9977784

Papasotiriou, Evangelos N. ; Boulogeorgos, Alexandros Apostolos A. ; De Guzman, Mar Francis et al. / Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes. 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022. IEEE, 2022. (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications).

@inproceedings{67b48f08d5334c099efcc80c211d598c,

title = "Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes",

abstract = "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.",

author = "Papasotiriou, {Evangelos N.} and Boulogeorgos, {Alexandros Apostolos A.} and {De Guzman}, {Mar Francis} and Katsuyuki Haneda and Angeliki Alexiou",

note = "Funding Information: This work has received funding from the European Commission Horizon 2020 research and innovation programme ARIADNE under grant agreement No. 871464. The multipath measured data used in this work were provided by Aalto University Finland. Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC ; Conference date: 12-09-2022 Through 15-09-2022",

year = "2022",

doi = "10.1109/PIMRC54779.2022.9977784",

language = "English",

series = "IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications",

publisher = "IEEE",

booktitle = "2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022",

address = "United States",

}

Papasotiriou, EN, Boulogeorgos, AAA, De Guzman, MF , Haneda, K & Alexiou, A 2022, Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes. julkaisussa 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications, IEEE, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Virtual, Online, Japani, 12/09/2022. https://doi.org/10.1109/PIMRC54779.2022.9977784

Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes. / Papasotiriou, Evangelos N.; Boulogeorgos, Alexandros Apostolos A.; De Guzman, Mar Francis et al.
2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022. IEEE, 2022. (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes

AU - Papasotiriou, Evangelos N.

AU - Boulogeorgos, Alexandros Apostolos A.

AU - De Guzman, Mar Francis

AU - Haneda, Katsuyuki

AU - Alexiou, Angeliki

N1 - Funding Information: This work has received funding from the European Commission Horizon 2020 research and innovation programme ARIADNE under grant agreement No. 871464. The multipath measured data used in this work were provided by Aalto University Finland. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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U2 - 10.1109/PIMRC54779.2022.9977784

DO - 10.1109/PIMRC54779.2022.9977784

M3 - Conference article in proceedings

AN - SCOPUS:85145666264

T3 - IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications

BT - 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022

PB - IEEE

T2 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

Y2 - 12 September 2022 through 15 September 2022

ER -

Papasotiriou EN, Boulogeorgos AAA, De Guzman MF , Haneda K, Alexiou A. Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes. julkaisussa 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022. IEEE. 2022. (IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications). doi: 10.1109/PIMRC54779.2022.9977784

Stochastic Characterization of outdoor Terahertz Channels Through Mixture Gaussian Processes

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

ARtificial Intelligence Aided D-band Network for 5G long term Evolution

Laitteet

Aalto Electronics-ICT

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