TY - GEN
T1 - Predictive QoS for Cellular-Connected UAV Communications
AU - Varghese, Ann
AU - Heikkinen, Antti
AU - Mähönen, Petri
AU - Ojanpera, Tiia
AU - Ahmad, Ijaz
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Unmanned aerial vehicles (UAVs), or drones, are transforming industries due to their affordability, ease of use, and adaptability. This emphasizes the need for reliable communication links, especially in beyond-line-of-sight scenarios. This paper investigates the feasibility of predicting future quality of service (QoS) in UAV payload communication links, with a special focus on 5G cellular technology. Through field tests conducted in a suburban environment, we explore challenges and trade-offs that cellular-connected UAVs face, particularly in the context of frequency band selection. We employed machine learning models to forecast uplink (UL) throughput for UAV payload communication, highlighting the significance of diverse training data for accurate predictions. The results reveal the effect of frequency band selection on UAV UL throughput rates at varying altitudes and the influence of integrating diverse feature sets, including radio, network, and spatial features, on ML model performance. These insights provide a foundation for addressing the complexities in UAV communications and enhancing UAV operations in modern networks.
AB - Unmanned aerial vehicles (UAVs), or drones, are transforming industries due to their affordability, ease of use, and adaptability. This emphasizes the need for reliable communication links, especially in beyond-line-of-sight scenarios. This paper investigates the feasibility of predicting future quality of service (QoS) in UAV payload communication links, with a special focus on 5G cellular technology. Through field tests conducted in a suburban environment, we explore challenges and trade-offs that cellular-connected UAVs face, particularly in the context of frequency band selection. We employed machine learning models to forecast uplink (UL) throughput for UAV payload communication, highlighting the significance of diverse training data for accurate predictions. The results reveal the effect of frequency band selection on UAV UL throughput rates at varying altitudes and the influence of integrating diverse feature sets, including radio, network, and spatial features, on ML model performance. These insights provide a foundation for addressing the complexities in UAV communications and enhancing UAV operations in modern networks.
KW - 5G
KW - 6G
KW - Machine Learning (ML)
KW - QoS
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85202826163&partnerID=8YFLogxK
U2 - 10.1109/ICC51166.2024.10623133
DO - 10.1109/ICC51166.2024.10623133
M3 - Conference article in proceedings
AN - SCOPUS:85202826163
T3 - IEEE International Conference on Communications
SP - 3901
EP - 3906
BT - ICC 2024 - IEEE International Conference on Communications
A2 - Valenti, Matthew
A2 - Reed, David
A2 - Torres, Melissa
PB - IEEE
T2 - IEEE International Conference on Communications
Y2 - 9 June 2024 through 13 June 2024
ER -