Performance Scaling of mmWave Personal IoT Networks (PINs) for XR Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU150978" target="_blank" >RIV/00216305:26220/23:PU150978 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10283621" target="_blank" >https://ieeexplore.ieee.org/document/10283621</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCWorkshops57953.2023.10283621" target="_blank" >10.1109/ICCWorkshops57953.2023.10283621</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Scaling of mmWave Personal IoT Networks (PINs) for XR Applications

Popis výsledku v původním jazyce

To provide a high-quality user experience in Extended Reality (XR) applications, high-throughput and low-latency communication is essential. A promising solution is the use of distributed networks operating in the higher frequency bands, such as millimeter-wave (mmWave) wearable Personal IoT Networks (PINs). However, in crowded environments, intra-network interactions can disrupt the Quality of Experience (QoE) for users. To improve the QoE, the understanding of the system-level performance trade-offs in these networks is important. This paper investigates the impact of various system parameters on the system-level performance of mmWave wearable PINs with 3D beamforming and data rate adaptation to the channel conditions in an environment with human body blockage. We employ an analytical methodology that combines stochastic geometry and queueing theory to devise an expression for the stationary distribution of the system and use it to compute the key metrics that describe the system-level performance. To assess mmWave PINs for XR in crowded environments, we examine the system operation trade-offs and explore the performance scaling.

Název v anglickém jazyce

Performance Scaling of mmWave Personal IoT Networks (PINs) for XR Applications

Popis výsledku anglicky

To provide a high-quality user experience in Extended Reality (XR) applications, high-throughput and low-latency communication is essential. A promising solution is the use of distributed networks operating in the higher frequency bands, such as millimeter-wave (mmWave) wearable Personal IoT Networks (PINs). However, in crowded environments, intra-network interactions can disrupt the Quality of Experience (QoE) for users. To improve the QoE, the understanding of the system-level performance trade-offs in these networks is important. This paper investigates the impact of various system parameters on the system-level performance of mmWave wearable PINs with 3D beamforming and data rate adaptation to the channel conditions in an environment with human body blockage. We employ an analytical methodology that combines stochastic geometry and queueing theory to devise an expression for the stationary distribution of the system and use it to compute the key metrics that describe the system-level performance. To assess mmWave PINs for XR in crowded environments, we examine the system operation trade-offs and explore the performance scaling.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023

ISBN

979-8-3503-3307-7

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1136-1142

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Roma

Datum konání akce

28. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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