Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication
The growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a...
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MDPI AG
2025-02-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/3/160 |
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| author | Donghao Li Binfang Du Zhiquan Bai |
| author_facet | Donghao Li Binfang Du Zhiquan Bai |
| author_sort | Donghao Li |
| collection | DOAJ |
| description | The growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a focus on covert communication under energy constraints. We propose a novel deep reinforcement learning (DRL) algorithm, Soft Actor-Critic for Covert Communication and Charging (SAC-CC), to address this problem. The SAC-CC algorithm maximizes the CCTR by dynamically allocating bandwidth for sensing and communication tasks while adjusting the UAV’s trajectory to manage energy consumption. This approach ensures accurate tracking of the adversarial UAV to maintain effective covert communication. Experimental results show that SAC-CC significantly outperforms existing DRL algorithms in CCTR and improves UAV endurance. Also, its robustness under different adversarial trajectories, covert communication requirements, and charging conditions is validated. Furthermore, the UAV’s flight altitude, along with the number and distribution pattern of adversarial UAVs, directly affect covert communication performance. Finally, the study emphasizes the trade-offs among bandwidth allocation, sensing accuracy, and the balance between power spectral density and UAV energy capacity, providing key insights for the practical configuration of bandwidth and energy parameters in UAV-assisted ISAC systems. |
| format | Article |
| id | doaj-art-959d1bd610b54711901aba92bcfbd4ff |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-959d1bd610b54711901aba92bcfbd4ff2025-08-20T02:42:42ZengMDPI AGDrones2504-446X2025-02-019316010.3390/drones9030160Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert CommunicationDonghao Li0Binfang Du1Zhiquan Bai2School of Information Science and Engineering, Shandong University, Qingdao 266237, ChinaSchool of Cyber Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao 266237, ChinaThe growing interest in integrated sensing and communication (ISAC) has accelerated the development of unmanned aerial vehicles (UAVs) and drones for secure data transmission. In this study, the optimization of UAV trajectory and bandwidth allocation within the ISAC framework is investigated, with a focus on covert communication under energy constraints. We propose a novel deep reinforcement learning (DRL) algorithm, Soft Actor-Critic for Covert Communication and Charging (SAC-CC), to address this problem. The SAC-CC algorithm maximizes the CCTR by dynamically allocating bandwidth for sensing and communication tasks while adjusting the UAV’s trajectory to manage energy consumption. This approach ensures accurate tracking of the adversarial UAV to maintain effective covert communication. Experimental results show that SAC-CC significantly outperforms existing DRL algorithms in CCTR and improves UAV endurance. Also, its robustness under different adversarial trajectories, covert communication requirements, and charging conditions is validated. Furthermore, the UAV’s flight altitude, along with the number and distribution pattern of adversarial UAVs, directly affect covert communication performance. Finally, the study emphasizes the trade-offs among bandwidth allocation, sensing accuracy, and the balance between power spectral density and UAV energy capacity, providing key insights for the practical configuration of bandwidth and energy parameters in UAV-assisted ISAC systems.https://www.mdpi.com/2504-446X/9/3/160covert communicationISACDRLUAV trajectorybandwidth allocationlimited energy |
| spellingShingle | Donghao Li Binfang Du Zhiquan Bai Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication Drones covert communication ISAC DRL UAV trajectory bandwidth allocation limited energy |
| title | Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication |
| title_full | Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication |
| title_fullStr | Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication |
| title_full_unstemmed | Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication |
| title_short | Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication |
| title_sort | deep reinforcement learning enabled trajectory and bandwidth allocation optimization for uav assisted integrated sensing and covert communication |
| topic | covert communication ISAC DRL UAV trajectory bandwidth allocation limited energy |
| url | https://www.mdpi.com/2504-446X/9/3/160 |
| work_keys_str_mv | AT donghaoli deepreinforcementlearningenabledtrajectoryandbandwidthallocationoptimizationforuavassistedintegratedsensingandcovertcommunication AT binfangdu deepreinforcementlearningenabledtrajectoryandbandwidthallocationoptimizationforuavassistedintegratedsensingandcovertcommunication AT zhiquanbai deepreinforcementlearningenabledtrajectoryandbandwidthallocationoptimizationforuavassistedintegratedsensingandcovertcommunication |