Downlink Performance Characterization of UAV-enabled Hybrid mmWave Networks using Poisson Point Processes
Keywords:
Geometry-based channel, Line-of-sight, Millimetre-wave, Stochastic geometry, Unmanned Aerial Vehicles (UAVs)Abstract
The increasing demand for rapid, reliable, and high-capacity wireless communication in emergency and infrastructure-deficient environments has accelerated interest in Unmanned Aerial Vehicle (UAV)-assisted cellular networks. In particular, the integration of UAVs with Millimetre-Wave (mmWave) communication presents a promising solution for providing on-demand coverage and high data rates due to the availability of large bandwidth and improved Line-of-Sight (LoS) conditions. However, mmWave propagation is highly sensitive to blockage, path loss, and Three-Dimensional (3D) geometry, making conventional terrestrial channel models inadequate for UAV-assisted deployments. This work develops a comprehensive geometry-based channel modelling framework for UAV-assisted hybrid cellular networks operating at sub-6 GHz (UHF) and mmWave frequency bands, with a focus on downlink performance in emergency deployment scenarios. The proposed model incorporates elevation-angle-dependent probabilistic LoS and non-LoS (NLoS) conditions, realistic path loss and shadowing characteristics, mmWave blockage effects, and directional antenna gain. Stochastic geometry is employed to model the spatial distribution of UAV base stations and ground users using Poisson Point Processes (PPP). Performance evaluation is conducted through extensive simulations, analysing key metrics such as path loss, Signal-to-Noise Ratio (SNR), achievable data rate, coverage probability, beamforming gain, and the impact of UAV altitude and density. The results demonstrate the existence of an optimal UAV altitude that balances LoS probability and distance-dependent path loss, highlight the strong dependence of mmWave performance on distance and blockage, and show that increasing UAV density significantly improves user data rate distribution. The proposed framework provides valuable insights for UAV deployment, network planning, and hybrid frequency utilization, and serves as a practical modeling foundation for future 5G/6G UAV-assisted mmWave communication systems, particularly in disaster recovery and public safety applications.
References
B. I. Bakare, T. S. Ibanibo, S. Orike, and C. O. Ahiakwo, “Assessing data rate improvements through traffic offloading to mmWave-UAVs in drone-assisted hybrid cellular networks,” Journal of Optoelectronics and Communication, vol. 7, no. 3, Jul. 2025.
T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun, “Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design,” IEEE Transactions on Communications, vol. 63, no. 9, pp. 3029–3056, Sep. 2015.
A. Al-Hourani, S. Kandeepan, and A. Jamalipour, “Modeling air-to-ground path loss for low-altitude platforms in urban environments,” in Proc. IEEE Global Communications Conference (GLOBECOM), Austin, TX, USA, 2014, pp. 2898–2904.
A. Merwaday and I. Guvenc, “UAV-assisted heterogeneous networks for public safety communications,” in Proc. IEEE Wireless Communications and Networking Conference Workshops (WCNCW), New Orleans, LA, USA, 2015, pp. 329–334.
Z. Zhang, Y. Liu, J. Huang, J. Zhang, J. Li, and R. He, “Channel characterization and modeling for 6G UAV-assisted emergency communications in complicated mountainous scenarios,” Sensors, vol. 23, no. 11, Art. no. 4998, May 2023.
K. Mao, Q. Zhu, M. Song, B. Hua, W. Zhong, and X. Ye, “A geometry-based beamforming channel model for UAV mmWave communications,” Sensors, vol. 20, no. 23, Art. no. 6957, Dec. 2020.
N. Moraitis, K. Psychogios, and A. D. Panagopoulos, “A survey of path loss prediction and channel models for unmanned aerial systems for system-level simulations,” Sensors, vol. 23, no. 10, Art. no. 4775, May 2023.
S. Ali, A. Abu-Samah, N. F. Abdullah, and N. L. Mohd Kamal, “Propagation modeling of unmanned aerial vehicle (UAV) 5G wireless networks in rural mountainous regions using ray tracing,” Drones, vol. 8, no. 7, Art. no. 334, Jul. 2024.
N. Xia, Y. Liu, and Y. Yu, “An elevation-aware large-scale channel model for UAV air-to-ground links,” Mathematics, vol. 13, no. 21, Art. no. 3377, Oct. 2025.
M. R. Akdeniz et al., “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1164–1179, Jun. 2014.
X. Wei, L. Peng, R. Xu, A. Li, X. Yu, and H. Wang, “Analysis of coverage and capacity for UAV-aided networks with directional mmWave communications,” Drones, vol. 8, no. 4, Art. no. 152, Apr. 2024.
H. Jung and I.-H. Lee, “Performance analysis of millimeter-wave UAV swarm networks under blockage effects,” Sensors, vol. 20, no. 16, Art. no. 4593, Aug. 2020.
M. Boschiero, M. Giordani, M. Polese, and M. Zorzi, “Coverage analysis of UAVs in millimeter wave networks: A stochastic geometry approach,” in Proc. Int. Wireless Communications and Mobile Computing Conf. (IWCMC), Limassol, Cyprus, 2020, pp. 351–357.
M. M. Selim, “Stochastic geometry analysis of UAV-assisted networks with probabilistic UAV activation,” Scientific Reports, vol. 15, no. 1, Oct. 2025.
F. Altheeb, I. Elshafiey, M. Altamimi, and A.-F. A. Sheta, “Customized millimeter wave channel model for enhancement of next-generation UAV-aided Internet of Things networks,” Sensors, vol. 24, no. 5, Art. no. 1528, Feb. 2024.
M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications,” IEEE Transactions on Wireless Communications, vol. 16, no. 11, pp. 7574–7589, Nov. 2017.
D.-H. Tran, V.-D. Nguyen, S. Chatzinotas, T. X. Vu, and B. Ottersten, “UAV relay-assisted emergency communications in IoT networks: Resource allocation and trajectory optimization,” IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1621–1637, Mar. 2022.
M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Optimal transport theory for cell association in UAV-enabled cellular networks,” IEEE Communications Letters, vol. 21, no. 9, pp. 2053–2056, Sep. 2017.
A. A. Khuwaja, Y. Chen, N. Zhao, M.-S. Alouini, and P. Dobbins, “A survey of channel modeling for UAV communications,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2804–2821, Fourth Quarter 2018.
J. Zhou, X. Zhang, K. Qin, F. Yang, and L. Wang, “Joint trajectory and power optimization for UAV-SAR based ISAC system,” IEEE Access, vol. 13, pp. 143465–143478, 2025.
M. Wang, X. Chen, S. Wang, and W. Shi, “RETRACTED: Three-dimensional geometry-based channel modeling and simulations for reconfigurable intelligent surface-assisted UAV-to-ground MIMO communications,” IET Communications, vol. 19, no. 1, Jan. 2024.
X. Wei, L. Peng, R. Xu, A. Li, X. Yu, and H. Wang, “3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition,” Computer Networks, vol. 248, Art. no. 110478, Jun. 2024.
M. Amine Ouamri, R. Alkanhel, C. Gueguen, M. A. Alohali, and S. S. M. Ghoneim, “Modeling and analysis of UAV-assisted mobile networks with imperfect beam alignment,” Computers, Materials & Continua, vol. 74, no. 1, pp. 453–467, 2023.
M. Mahbub et al., “UAV-assisted wireless communications in the 6G-and-beyond era: An extensive survey on characteristics, standardization and regulations, enabling technologies, challenges, and future directions,” Vehicular Communications, vol. 56, Art. no. 100977, Dec. 2025.
