| Autor: |
Yaxi Liu, Xulong Li, Boxin He, Meng Gu, Wei Huangfu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Drones, Vol 8, Iss 11, p 647 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2504-446X |
| DOI: |
10.3390/drones8110647 |
| Popis: |
Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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