TY - GEN
T1 - Covertness-Aware Trajectory Design for UAV
T2 - 2022 IEEE International Conference on Communications, ICC 2022
AU - Li, Yuanjian
AU - Aghvami, A. Hamid
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the presence of Warden's detection, a maximization problem on transmission throughput from unmanned aerial vehicle (UAV) to legitimate nodes is considered and solved via UAV trajectory design, subject to covert, velocity and mobility constraints. With the building-distribution-based pathloss model and the Warden's uncertain location model, the formulated optimization problem is challenging to be tackled through standard offline optimization methods. Alternatively, a twin delayed deep deterministic policy gradient (TD3) approach enhanced by multi-step learning and prioritized experience replay (PER) techniques, termed as multi-step TD3-PER, is proposed to help the UAV adaptively select velocity from continuous action space. Numerical results demonstrate the effectiveness of the proposed multi-step TD3-PER solution and showcase the corresponding superiorities against provided baselines.
AB - In the presence of Warden's detection, a maximization problem on transmission throughput from unmanned aerial vehicle (UAV) to legitimate nodes is considered and solved via UAV trajectory design, subject to covert, velocity and mobility constraints. With the building-distribution-based pathloss model and the Warden's uncertain location model, the formulated optimization problem is challenging to be tackled through standard offline optimization methods. Alternatively, a twin delayed deep deterministic policy gradient (TD3) approach enhanced by multi-step learning and prioritized experience replay (PER) techniques, termed as multi-step TD3-PER, is proposed to help the UAV adaptively select velocity from continuous action space. Numerical results demonstrate the effectiveness of the proposed multi-step TD3-PER solution and showcase the corresponding superiorities against provided baselines.
KW - covert communication
KW - deep reinforcement learning
KW - Drone
KW - trajectory design
UR - http://www.scopus.com/inward/record.url?scp=85137268880&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9839093
DO - 10.1109/ICC45855.2022.9839093
M3 - Conference Proceeding
AN - SCOPUS:85137268880
T3 - IEEE International Conference on Communications
SP - 7
EP - 12
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 May 2022 through 20 May 2022
ER -