Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems
This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy ha...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10980338/ |
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| author | Van Nhan Vo Nguyen Quoc Long Viet-Hung Dang Tu Dac Ho Hung Tran Symeon Chatzinotas Dinh-Hieu Tran Surasak Sanguanpong Chakchai So-In |
| author_facet | Van Nhan Vo Nguyen Quoc Long Viet-Hung Dang Tu Dac Ho Hung Tran Symeon Chatzinotas Dinh-Hieu Tran Surasak Sanguanpong Chakchai So-In |
| author_sort | Van Nhan Vo |
| collection | DOAJ |
| description | This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. |
| format | Article |
| id | doaj-art-2ee782e7315f41038248cb4b1cecf9ed |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-2ee782e7315f41038248cb4b1cecf9ed2025-08-20T03:43:55ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164140415510.1109/OJCOMS.2025.356576410980338Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive SystemsVan Nhan Vo0https://orcid.org/0000-0003-0753-5203Nguyen Quoc Long1Viet-Hung Dang2Tu Dac Ho3https://orcid.org/0000-0001-7215-0479Hung Tran4Symeon Chatzinotas5https://orcid.org/0000-0001-5122-0001Dinh-Hieu Tran6https://orcid.org/0000-0002-6328-3103Surasak Sanguanpong7https://orcid.org/0000-0002-7045-7394Chakchai So-In8https://orcid.org/0000-0003-1026-191XFaculty of Information Technology, Duy Tan University, Da Nang, VietnamFaculty of Information Technology, Duy Tan University, Da Nang, VietnamFaculty of Information Technology, Duy Tan University, Da Nang, VietnamDepartment of Information Security and Communication Technology, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, NorwayDATCOM Lab, Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University, Hanoi, VietnamInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, LuxembourgDepartment of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, ThailandApplied Network Technology, College of Computing, Khon Kaen University, Khon Kaen, ThailandThis paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions.https://ieeexplore.ieee.org/document/10980338/Cognitive radio (CR)nonorthogonal multiple access (NOMA)unmanned aerial vehicle (UAV)RIScovert communication |
| spellingShingle | Van Nhan Vo Nguyen Quoc Long Viet-Hung Dang Tu Dac Ho Hung Tran Symeon Chatzinotas Dinh-Hieu Tran Surasak Sanguanpong Chakchai So-In Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems IEEE Open Journal of the Communications Society Cognitive radio (CR) nonorthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) RIS covert communication |
| title | Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems |
| title_full | Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems |
| title_fullStr | Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems |
| title_full_unstemmed | Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems |
| title_short | Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems |
| title_sort | deep learning driven throughput maximization in covert communication for uav ris cognitive systems |
| topic | Cognitive radio (CR) nonorthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) RIS covert communication |
| url | https://ieeexplore.ieee.org/document/10980338/ |
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