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An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay

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Passive and Active Measurement (PAM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13882))

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  • The original version of this chapter was revised: An error in the presentation of Jaideep Chandrashekar’s affiliation was corrected. The correction to this chapter is available at https://doi.org/10.1007/978-3-031-28486-1_28

Abstract

5G aims to offer not only significantly higher throughput than previous generations of cellular networks, but also promises millisecond (ms) and sub-millisecond (ultra-)low latency support at the 5G physical (PHY) layer for future applications. While prior measurement studies have confirmed that commercial 5G deployments can achieve up to several Gigabits per second (Gbps) throughput (especially with the mmWave 5G radio), are they able to deliver on the (sub) millisecond latency promise? With this question in mind, we conducted to our knowledge the first in-depth measurement study of commercial 5G mmWave PHY latency using detailed physical channel events and messages. Through carefully designed experiments and data analytics, we dissect various factors that influence 5G PHY latency of both downlink and uplink data transmissions, and explore their impacts on end-to-end delay. We find that while in the best cases, the 5G (mmWave) PHY-layer is capable of delivering ms/sub-ms latency (with a minimum of 0.09 ms for downlink and 0.76 ms for uplink), these happen rarely. A variety of factors such as channel conditions, re-transmissions, physical layer control and scheduling mechanisms, mobility, and application (edge) server placement can all contribute to increased 5G PHY latency (and thus end-to-end (E2E) delay). Our study provides insights to 5G vendors, carriers as well as application developers/content providers on how to better optimize or mitigate these factors for improved 5G latency performance.

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Change history

  • 05 April 2023

    A correction has been published.

Notes

  1. 1.

    The one-hope (UE to gNB) target for URLLC “should be 0.5ms for UL, and 0.5ms for DL”.

  2. 2.

    Our definition of region in this paper is as per AWS, and it is a cluster of a minimum of 3 data centers.

  3. 3.

    The primary physical channel for the DL transmissions (base station to UE) is PDSCH (physical downlink shared channel), and for the UL transmissions (UE to base station) is PUSCH (physical uplink shared channel).

  4. 4.

    Assuming no spatial multiplexing, which is the case of VZW 5G mmWave. However, with spatial multiplexing, at most 2 Transport Blocks can be transmitted per \(slot\).

  5. 5.

    This data schematics corresponds to the DCI as shown in Fig. 2.

  6. 6.

    Defined as the area between two 5G towers A and B where HO occurs from tower A to B or vice versa.

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Acknowledgements

This research was supported in part by NSF under Grants CNS-1901103, CNS-1915122, CNS-2038559, CNS-21544078, CNS-2128489, CNS-2220286, CCF-2212318 and CNS-2220292 as well as a Cisco Research Award and an InterDigital gift.

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Authors and Affiliations

  1. University of Minnesota, Twin Cities, USA

    Rostand A. K. Fezeu, Eman Ramadan, Wei Ye, Benjamin Minneci, Jack Xie, Arvind Narayanan, Ahmad Hassan, Feng Qian & Zhi-Li Zhang

  2. InterDigital, Los Altos, USA

    Jaideep Chandrashekar

  3. Cisco Systems, San Jose, USA

    Myungjin Lee

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Correspondence to Rostand A. K. Fezeu .

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Editors and Affiliations

  1. Karlstad University, Karlstad, Sweden

    Anna Brunstrom

  2. Edgio, Scottsdale, AZ, USA

    Marcel Flores

  3. IMDEA Networks Institute, Madrid, Spain

    Marco Fiore

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Fezeu, R.A.K. et al. (2023). An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay. In: Brunstrom, A., Flores, M., Fiore, M. (eds) Passive and Active Measurement. PAM 2023. Lecture Notes in Computer Science, vol 13882. Springer, Cham. https://doi.org/10.1007/978-3-031-28486-1_13

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