An Experimental mmWave Channel Model for UAV-to-UAV Communications
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Abstract (arXiv)
Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high-bandwidth wireless technologies for both radio access and backhaul. With this respect, the millimeter wave (mmWave) spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra-high data-rates for aerial platforms. Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro-mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments. To fill this gap, we propose an empirical propagation loss model for UAV-to-UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2021Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulationcited by 11
- 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networkscited by 8
- 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarmscited by 0
- 2026Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deploymentcited by 0
- 2026Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment17 Mar 2026
- 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms11 Jan 2025
- 2021Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation20 Oct 2021
- 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks2 Feb 2021