A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917893016387584 |
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| author | Bai, Lu Lu, Mengyuan Huang, Ziwei Cheng, Xiang |
| author_facet | Bai, Lu Lu, Mengyuan Huang, Ziwei Cheng, Xiang |
| contents | In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications Bai, Lu Lu, Mengyuan Huang, Ziwei Cheng, Xiang Signal Processing In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model. |
| title | A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.08825 |