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A Three-Dimensional Path Loss Model for THz Band Aerial Communications

Sina Jorjani, Caglar Tunc, Ozgur Gurbuz, Akhtar Saeed

Abstract

Accurate characterization of Terahertz (THz) band path loss is critical for reliable high-frequency communication, especially in aerial networks where transceivers may operate at different altitudes. Existing THz-band path loss models for aerial networks focus on horizontal or vertical transceiver deployments, and fall short at modeling the random 3D geometry of transceiver locations. To address this limitation, we propose a new analytical THz path loss model that incorporates arbitrary 3D geometry of transceiver locations and frequency-selective absorption, obtained through a two-dimensional regression. We validate our proposed model with the propagation data collected via the Atmospheric Model (am) tool for multiple aerial link types, including drone-to-drone (Dr2Dr), medium-altitude aerial communication (MAAC), high-altitude unmanned aerial vehicles~(UAV)-to-UAV (U2U) links over varying transceiver separation and sub-THz to low-THz spectrum, i.e., 0.1--1~THz. The proposed framework provides a unified and accurate model for analyzing and designing future high-frequency aerial communication systems.

A Three-Dimensional Path Loss Model for THz Band Aerial Communications

Abstract

Accurate characterization of Terahertz (THz) band path loss is critical for reliable high-frequency communication, especially in aerial networks where transceivers may operate at different altitudes. Existing THz-band path loss models for aerial networks focus on horizontal or vertical transceiver deployments, and fall short at modeling the random 3D geometry of transceiver locations. To address this limitation, we propose a new analytical THz path loss model that incorporates arbitrary 3D geometry of transceiver locations and frequency-selective absorption, obtained through a two-dimensional regression. We validate our proposed model with the propagation data collected via the Atmospheric Model (am) tool for multiple aerial link types, including drone-to-drone (Dr2Dr), medium-altitude aerial communication (MAAC), high-altitude unmanned aerial vehicles~(UAV)-to-UAV (U2U) links over varying transceiver separation and sub-THz to low-THz spectrum, i.e., 0.1--1~THz. The proposed framework provides a unified and accurate model for analyzing and designing future high-frequency aerial communication systems.

Paper Structure

This paper contains 9 sections, 8 equations, 7 figures, 3 tables.

Figures (7)

  • Figure 1: Dr2Dr communication scenario in 3D, showing distances $d$, $d_h$, and $d_v$, zenith angle $\theta$, and the sea level at $z = 0$.
  • Figure 2: Cascaded-regression pipeline. The input $\tau(l,d_h,d_v,f)$ splits into horizontal (blue) and vertical (red) components and is finally multiplied to form $\hat{\tau}(l,d_h,d_v,f)$.
  • Figure 3: Example path loss curves from the am-tool and the proposed $\theta$-agnostic and $\theta$-adaptive estimates for $l=8$ km, $d=3$ km, and $\theta=9^{\circ}$ in the MAAC scenario, shown across all sub-bands.
  • Figure 4: Dr2Dr scenario. NRMSE of the $\theta$-agnostic and $\theta$-adaptive path loss estimations as a function of (a) communication distance $d$ in km; (b) the minimum altitude of the transceivers $l$ in km; and (c) zenith angle $\theta$ in degrees.
  • Figure 5: MAAC scenario: NRMSE of the $\theta$-agnostic and $\theta$-adaptive path loss estimations as a function of (a) communication distance $d$ in km; (b) the minimum altitude of the transceivers $l$ in km; and (c) zenith angle $\theta$ in degrees.
  • ...and 2 more figures