UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916090021412864 |
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| author | Wang, Zao Xu, Lianming Hou, Luyang Li, Ruoguang Wang, Li |
| author_facet | Wang, Zao Xu, Lianming Hou, Luyang Li, Ruoguang Wang, Li |
| contents | UAV-assisted integrated sensing and communication (ISAC) network is crucial for post-disaster emergency rescue. The speed of UAV deployment will directly impact rescue results. However, the ISAC UAV deployment in emergency scenarios is difficult to solve, which contradicts the rapid deployment. In this paper, we propose a two-stage deployment framework to achieve rapid ISAC UAV deployment in emergency scenarios, which consists of an offline stage and an online stage. Specifically, in the offline stage, we first formulate the ISAC UAV deployment problem and define the ISAC utility as the objective function, which integrates communication rate and localization accuracy. Secondly, we develop a dynamic particle swarm optimization (DPSO) algorithm to construct an optimized UAV deployment dataset. Finally, we train a convolutional neural network (CNN) model with this dataset, which replaces the time-consuming DPSO algorithm. In the online stage, the trained CNN model can be used to make quick decisions for the ISAC UAV deployment. The simulation results indicate that the trained CNN model achieves superior ISAC performance compared to the classic particle swarm optimization algorithm. Additionally, it significantly reduces the deployment time by more than 96%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach Wang, Zao Xu, Lianming Hou, Luyang Li, Ruoguang Wang, Li Networking and Internet Architecture Signal Processing UAV-assisted integrated sensing and communication (ISAC) network is crucial for post-disaster emergency rescue. The speed of UAV deployment will directly impact rescue results. However, the ISAC UAV deployment in emergency scenarios is difficult to solve, which contradicts the rapid deployment. In this paper, we propose a two-stage deployment framework to achieve rapid ISAC UAV deployment in emergency scenarios, which consists of an offline stage and an online stage. Specifically, in the offline stage, we first formulate the ISAC UAV deployment problem and define the ISAC utility as the objective function, which integrates communication rate and localization accuracy. Secondly, we develop a dynamic particle swarm optimization (DPSO) algorithm to construct an optimized UAV deployment dataset. Finally, we train a convolutional neural network (CNN) model with this dataset, which replaces the time-consuming DPSO algorithm. In the online stage, the trained CNN model can be used to make quick decisions for the ISAC UAV deployment. The simulation results indicate that the trained CNN model achieves superior ISAC performance compared to the classic particle swarm optimization algorithm. Additionally, it significantly reduces the deployment time by more than 96%. |
| title | UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2401.07001 |