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Connectivity Analysis for Large-Scale Intelligent Reflecting Surface Aided mmWave Cellular Networks

Wang, Yi ; Xiang, Lin ; Zhang, Jing ; Ge, Xiaohu (2022)
Connectivity Analysis for Large-Scale Intelligent Reflecting Surface Aided mmWave Cellular Networks.
33rd International Symposium on Personal, Indoor and Mobile Radio Communications. virtual Conference (12.09.2022-15.09.2022)
doi: 10.1109/PIMRC54779.2022.9977979
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Autor(en): Wang, Yi ; Xiang, Lin ; Zhang, Jing ; Ge, Xiaohu
Art des Eintrags: Bibliographie
Titel: Connectivity Analysis for Large-Scale Intelligent Reflecting Surface Aided mmWave Cellular Networks
Sprache: Englisch
Publikationsjahr: 20 Dezember 2022
Veranstaltungstitel: 33rd International Symposium on Personal, Indoor and Mobile Radio Communications
Veranstaltungsort: virtual Conference
Veranstaltungsdatum: 12.09.2022-15.09.2022
DOI: 10.1109/PIMRC54779.2022.9977979
Kurzbeschreibung (Abstract):

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

Freie Schlagworte: emergenCITY, emergenCITY_KOM, Open6GHub
Zusätzliche Informationen:

BMBF Open6GHub

Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 01 Feb 2023 12:19
Letzte Änderung: 03 Apr 2024 13:05
PPN: 507397886
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