4.7 Article

Statistical Analysis and Modeling of User Micromobility for THz Cellular Communications

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 71, Issue 1, Pages 725-738

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3124870

Keywords

6G; terahertz; micromobility; measurements; applications; statistical analysis; outage

Funding

  1. Russian Science Foundation [21-79-10139]
  2. Russian Science Foundation [21-79-10139] Funding Source: Russian Science Foundation

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This paper investigates the importance of Terahertz wireless access in sixth generation cellular systems and measures the micromobility process of various applications. The obtained results are crucial for mathematical modeling and system-level analysis of THz cellular systems.
Terahertz (THz, 0.3-3 THz) wireless access is nowadays considered as a major enabling technology for sixth generation (6G) cellular systems. To compensate for high propagation losses, these systems will utilize antenna arrays with extremely directional beams. The performance of such systems will thus be heavily affected by micromobility such as shakes and rotations of user equipment (UE) even when user is in stationary position. The ultimate impact of micromobility is spontaneous degradation of signal-to-noise (SNR) level leading to outages. In this paper, we measure and statistically investigate the micromobility process of various applications including video viewing, phone calling, virtual reality viewing and racing game. Particularly, we characterize occupancy distributions and first-passage time (FPT) to outage for various antenna configurations. We also assess the radial symmetry in micromobility patterns and characterize distance-dependent velocity and drift to the origin parameters. The obtained results are essential for developing mathematical models of micromobility patterns that needs to be further used in system-level analysis of THz cellular systems. To this end, we also illustrate that Markov models are only suitable for applications with low and purely random micromobility dynamics such as video viewing and phone calling. When a user is controlled by the application, as in the case of gaming, Markov models overestimate FPT to outage.

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