TY - JOUR
T1 - Robust Trajectory and Resource Allocation for UAV Communications in Uncertain Environments With No-Fly Zone
T2 - A Deep Learning Approach
AU - Lee, Woongsup
AU - Lee, Kisong
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, we investigate robust trajectory design and resource allocation in unmanned aerial vehicle (UAV) enabled wireless networks to maximize the minimum average spectral efficiency (SE) among mobile nodes (MNs) on the ground while coping with uncertainties in trajectory. Our work specifically addresses practical challenges encountered during trajectory planning, namely: 1) the mobility of MNs, causing changes in their locations over time; 2) the positioning error of UAV, leading to deviations from its planned trajectory; and 3) the presence of no-fly zones (NFZs), which must be avoided during UAV flight. Taking these practical aspects into account, we propose a deep learning (DL) framework that integrates a deep neural network (DNN) structure with an unsupervised learning-based training methodology. The former enables efficient modeling of UAV trajectory and resource allocation, while the latter allows effective training of DNNs without labeled data. Through performance evaluations, we demonstrate that the proposed DL-based scheme outperforms the comparative baseline schemes in terms of the minimum average SE by optimizing trajectory and resource allocation with low computation time. Furthermore, we validate the robustness of our proposed scheme against the uncertainty associated with positioning errors.
AB - In this paper, we investigate robust trajectory design and resource allocation in unmanned aerial vehicle (UAV) enabled wireless networks to maximize the minimum average spectral efficiency (SE) among mobile nodes (MNs) on the ground while coping with uncertainties in trajectory. Our work specifically addresses practical challenges encountered during trajectory planning, namely: 1) the mobility of MNs, causing changes in their locations over time; 2) the positioning error of UAV, leading to deviations from its planned trajectory; and 3) the presence of no-fly zones (NFZs), which must be avoided during UAV flight. Taking these practical aspects into account, we propose a deep learning (DL) framework that integrates a deep neural network (DNN) structure with an unsupervised learning-based training methodology. The former enables efficient modeling of UAV trajectory and resource allocation, while the latter allows effective training of DNNs without labeled data. Through performance evaluations, we demonstrate that the proposed DL-based scheme outperforms the comparative baseline schemes in terms of the minimum average SE by optimizing trajectory and resource allocation with low computation time. Furthermore, we validate the robustness of our proposed scheme against the uncertainty associated with positioning errors.
KW - Unmanned aerial vehicle
KW - deep learning
KW - mobility
KW - no-fly-zone
KW - optimization
KW - positioning error
KW - trajectory
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85194092162&partnerID=8YFLogxK
U2 - 10.1109/TITS.2024.3399913
DO - 10.1109/TITS.2024.3399913
M3 - Article
AN - SCOPUS:85194092162
SN - 1524-9050
VL - 25
SP - 14233
EP - 14244
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 10
ER -