M. P. R, Sai Kiran

The next-generation wireless communication technologies, such as 5G Frequency Range 2 (FR2), WLANs, etc., are utilizing the millimeter-wave (mmWave) spectrum for increased data rates. For example, applications involving unmanned aerial vehicles (UAVs) for coverage extension in 5G comprise a huge amount of user data to be transmitted to the gNodeB (gNB). Hence, utilizing mmWave communication technologies such as 5G FR2 to establish the link between the UAV and gNB can be a suitable option. However, one of the critical technologies of 5G FR2 is the utilization of directional beamforming, and the impact of the axial unsteadiness of UAV s on the coverage needs to be carefully analyzed. This paper studies and models the effect of beam misalignment manifested due to axial disturbances in UAVs while hovering at a fixed altitude on the network performance. Firstly, a stochastic model for beam misalignment is developed by collecting real-time inertial data from a hovering UAV over multiple flights. Second, an analytical formulation of the coverage probability in a directional UAV to gNB mmWave communication link is formulated. The performance analysis shows that the analytical model matches the simulation outcomes with less than 2% error.

Non-terrestrial networks (NTNs) typically consist of UAV swarms equipped with multiple sensors that generate massive data, requiring real-time communication to a gateway for further processing. Hence, millimeter-wave (mmWave) communication technologies operating above 24 GHz emerge as a suitable solution for enabling high-speed intra-UAV swarm communication. However, mmWave communication technologies use multiple antenna-based directional beamforming for improved coverage, which leads to higher power consumption and frequent beam training overhead, affecting swarm endurance. To address this, we propose a novel optimal transmit power allocation scheme that enhances swarm endurance and improves throughput by reducing beam training overhead. Firstly, the proposed scheme uses the Kalman filter (in this paper, but not limited to) at the transmitting UAV to estimate the real-time position of the receiving UAV. The estimated position is utilized to calculate path loss and select the optimal transmit power level needed to meet the required received signal power threshold at the receiving UAV. To reduce outages from errors in UAV position estimation, the proposed scheme also adjusts the transmit power level by incorporating an additional buffer distance around the estimated position, thereby enhancing reliability with a minimal increase in transmit power. The performance analysis shows that the proposed scheme achieves an average reliability of more than 99% and power savings of up to 49.4% while increasing the throughput under saturated traffic conditions, thus establishing its effectiveness in mobile UAV swarms. Also, the proposed scheme is compared with three popular mechanisms existing in the literature: 1) baseline approach where constant transmit power is utilized, 2) deep learning (long short-term memory, LSTM) based transmit power allocation, and 3) power allocation using α−β−γ filter for receiving UAV position estimation. The performance comparison shows that the proposed scheme offers superior performance in terms of power savings, reliability, throughput, and computational complexity. © 2025 Elsevier B.V., All rights reserved.