Ray-Based Modeling of Unlicensed-Band mmWave Propagation Inside a City Bus

In the wake of recent hardware developments, augmented, mixed, and virtual reality applications – grouped under an umbrella term of eXtended reality (XR) – are believed to have a transformative effect on customer experience. Among many XR use cases, of particular interest are crowded commuting scenarios, in which passengers are involved in in-bus/in-train entertainment, e.g., high-quality video or 3D hologram streaming and AR/VR gaming. In the case of a city bus, the number of commuting users during the busy hours may exceed forty, and, hence, could pose far higher traffic demands than the existing microwave technologies can support. Consequently, the carrier candidate for XR hardware should be sought in the millimeter-wave (mmWave) spectrum; however, the use of mmWave cellular frequencies may appear impractical due to the severe attenuation or blockage by the modern metal coating of the glass. As a result, intra-vehicle deployment of unlicensed mmWave access points becomes the most promising solution for bandwidth-hungry XR devices. In this paper, we present the calibrated results of shooting-and-bouncing ray simulation at 60 GHz for the bus interior. We analyze the delay and angular spread, estimate the parameters of the Saleh-Valenzuela channel model, and draw important practical conclusions regarding the intra-vehicle propagation at 60 GHz. © 2019, Springer Nature Switzerland AG.

Авторы
Ponomarenko-Timofeev A.1 , Ometov A. 1 , Galinina O. 1, 2
Язык
Английский
Страницы
269-281
Статус
Опубликовано
Том
11660 LNCS
Год
2019
Организации
  • 1 Tampere University, Tampere, Finland
  • 2 Peoples’ Friendship University of Russia (RUDN University), Moscow, Russian Federation
Ключевые слова
Channel model; Intra-vehicular; mmWave; SBR; Wearables
Дата создания
24.12.2019
Дата изменения
24.12.2019
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/55331/
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Другие записи

Manariyo S., Poluektov D., Abdukodir K., Muthanna A., Makolkina M.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Том 11660 LNCS. 2019. С. 562-571