Deep Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Enabled Secure Cognitive Non-Terrestrial Networks
This paper proposes learning-based joint optimization of unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-assisted cognitive non-terrestrial networks (NTNs) to enhance the secrecy performance. The practical RIS phase shift model...
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| Veröffentlicht in: | IEEE wireless communications letters 2024-01, Vol.13 (1), p.1-1 |
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| container_title | IEEE wireless communications letters |
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| creator | Liu, Yun Huang, Chong Chen, Gaojie Song, Ruiliang Song, Shutian Xiao, Pei |
| description | This paper proposes learning-based joint optimization of unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-assisted cognitive non-terrestrial networks (NTNs) to enhance the secrecy performance. The practical RIS phase shift model, outdated channel state information (CSI) and interference from neighboring satellites are considered. We introduce a deep reinforcement learning (DRL) algorithm to solve the UAV trajectory optimization problem to enhance the gain from RIS. Furthermore, we propose a double cascade correlation network (DCCN) to adjust the RIS reflection coefficients in UAV trajectory optimization. Simulation results show that the proposed algorithms significantly improve the secrecy performance in UAV-RIS-assisted cognitive NTNs. |
| doi_str_mv | 10.1109/LWC.2023.3325066 |
| format | Article |
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The practical RIS phase shift model, outdated channel state information (CSI) and interference from neighboring satellites are considered. We introduce a deep reinforcement learning (DRL) algorithm to solve the UAV trajectory optimization problem to enhance the gain from RIS. Furthermore, we propose a double cascade correlation network (DCCN) to adjust the RIS reflection coefficients in UAV trajectory optimization. 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| subjects | Algorithms Autonomous aerial vehicles Beamforming cognitive non-terrestrial network Deep learning Interference Machine learning physical layer security Reconfigurable intelligent surface Reflection Reflection coefficient Rician channels Satellites Signal to noise ratio Trajectory optimization unmanned aerial vehicle Unmanned aerial vehicles |
| title | Deep Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Enabled Secure Cognitive Non-Terrestrial Networks |
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