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Quantifying the Density of mmWave NR Deployments for Provisioning Multi-Layer VR Services

Author

Listed:
  • Vitalii Beschastnyi

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Daria Ostrikova

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Roman Konyukhov

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Elizaveta Golos

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Alexander Chursin

    (Department of Applied Economics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Dmitri Moltchanov

    (Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland)

  • Yuliya Gaidamaka

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia
    Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 119333 Moscow, Russia)

Abstract

The 5G New Radio (NR) technology operating in millimeter wave (mmWave) frequency band is designed for support bandwidth-greedy applications requiring extraordinary rates at the access interface. However, the use of directional antenna radiation patterns, as well as extremely large path losses and blockage phenomenon, requires efficient algorithms to support these services. In this study, we consider the multi-layer virtual reality (VR) service that utilizes multicast capabilities for baseline layer and unicast transmissions for delivering an enhanced experience. By utilizing the tools of stochastic geometry and queuing theory we develop a simple algorithm allowing to estimate the deployment density of mmWave NR base stations (BS) supporting prescribed delivery guarantees. Our numerical results show that the highest gains of utilizing multicast service for distributing base layer is observed for high UE densities. Despite of its simplicity, the proposed multicast group formation scheme operates close to the state-of-the-art algorithms utilizing the widest beams with longest coverage distance in approximately 50–70% of cases when UE density is λ ≥ 0.3 . Among other parameters, QoS profile and UE density have a profound impact on the required density of NR BSs while the effect of blockers density is non-linear having the greatest impact on strict QoS profiles. Depending on the system and service parameters the required density of NR BSs may vary in the range of 20–250 BS/km 2 .

Suggested Citation

  • Vitalii Beschastnyi & Daria Ostrikova & Roman Konyukhov & Elizaveta Golos & Alexander Chursin & Dmitri Moltchanov & Yuliya Gaidamaka, 2021. "Quantifying the Density of mmWave NR Deployments for Provisioning Multi-Layer VR Services," Future Internet, MDPI, vol. 13(7), pages 1-16, July.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:7:p:185-:d:597451
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    References listed on IDEAS

    as
    1. Mika Hoppari & Mikko Uitto & Jukka Mäkelä & Ilkka Harjula & Seppo Rantala, 2021. "Performance of the 5th Generation Indoor Wireless Technologies-Empirical Study," Future Internet, MDPI, vol. 13(7), pages 1-11, July.
    2. Anil Kumar Karembai & Jeffrey Thompson & Patrick Seeling, 2018. "Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service," Future Internet, MDPI, vol. 10(7), pages 1-12, July.
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