TY - GEN
T1 - MATCHING COMBINED MULTI-AGENT REINFORCEMENT LEARNING FOR UAV SECURE DATA DISSEMINATION
AU - Chen, Kaiyue
AU - Gao, Ang
AU - Duan, Weijun
AU - Liang, Wei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Due to the high flexibility and mobility, unmanned aerial vehicles (UAVs) can be deployed as aerial relays touring to disseminate data to ground users (GUs), especially when the ground base station is temporally dysfunctional or damaged. However, the broadcasting nature of wireless communication leads to the security issue with the presence of malicious eavesdroppers (Eves). The paper proposes a matching combined multi-agent deep reinforcement learning (DRL) to maximize the average secure rate for UAV-ground communications in probabilistic line-of-sight (LoS) channels, with the joint consideration of UAVs' propulsion energy and trajectory, as well as GUs' dissemination data size. The numerical simulation demonstrates that comparing with the other no matching combined DRL or valued-based DRL approaches, the proposed matching combined multi-agent deep deterministic policy gradient (matching-MADDPG) has better performance at both trajectory and convergence.
AB - Due to the high flexibility and mobility, unmanned aerial vehicles (UAVs) can be deployed as aerial relays touring to disseminate data to ground users (GUs), especially when the ground base station is temporally dysfunctional or damaged. However, the broadcasting nature of wireless communication leads to the security issue with the presence of malicious eavesdroppers (Eves). The paper proposes a matching combined multi-agent deep reinforcement learning (DRL) to maximize the average secure rate for UAV-ground communications in probabilistic line-of-sight (LoS) channels, with the joint consideration of UAVs' propulsion energy and trajectory, as well as GUs' dissemination data size. The numerical simulation demonstrates that comparing with the other no matching combined DRL or valued-based DRL approaches, the proposed matching combined multi-agent deep deterministic policy gradient (matching-MADDPG) has better performance at both trajectory and convergence.
KW - Deep Reinforcement Learning
KW - Multi-Agent Deep Deterministic Policy Gradient
KW - Secure Rate
UR - http://www.scopus.com/inward/record.url?scp=85140368653&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9883364
DO - 10.1109/IGARSS46834.2022.9883364
M3 - 会议稿件
AN - SCOPUS:85140368653
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3367
EP - 3370
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -