UAV-STAR-RIS Aided Covert Multicasting in Low-Altitude Network
Security and privacy have become pressing concerns in low-altitude network (LAN), mainly due to its high mobility, and the critical reliance on line of sight (LoS) communication. To address these threats, covert communication is introduced into LAN to further enhance the transmission security by pro...
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| Veröffentlicht in: | IEEE transactions on network science and engineering S. 1 - 18 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
18.12.2025
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| Schlagworte: | |
| ISSN: | 2334-329X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Security and privacy have become pressing concerns in low-altitude network (LAN), mainly due to its high mobility, and the critical reliance on line of sight (LoS) communication. To address these threats, covert communication is introduced into LAN to further enhance the transmission security by protecting the transmission behavior from being detected. In this paper, we introduced the unmanned aerial vehicle-mounted simultaneous transmitting and reflecting reconfigurable intelligent surfaces (UAV-STAR-RIS) into the covert multicasting LAN. Specifically, Alice transmits covert signals to Bobs via the UAV-STAR-RIS against two wardens. With the object to maximize the minimum achievable covert rate, we jointly optimize the transmit beamforming of Alice, the reflection and transmission coefficients of the STAR-RIS, and the trajectory of the UAV-STAR-RIS. Firstly, an iterative algorithmic framework combining alternating optimization (AO), successive convex approximation (SCA), and penalty-based semi-definite relaxation (SDR) is developed to optimize transmit beamforming and STAR-RIS coefficients under fixed UAV-STAR-RIS positions. Then, the trajectory of the UAV-STAR-RIS is optimized by utilizing the proximal policy optimization (PPO) algorithm. Numerical results indicate that our proposed scheme shows superiority in covert communication performance compared with benchmarks. |
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| ISSN: | 2334-329X |
| DOI: | 10.1109/TNSE.2025.3646148 |