To support the development of multiple-unmanned aerial vehicle (multi-UAV) cooperation in emergency communications, an in-depth analysis of multi-UAV cooperative channel statistical properties is conducted in this paper. Based on the wireless channel simulation software, i.e., Wireless InSite, the ray-tracing technology based multi-UAV cooperative communication channel data in the urban earthquake rescue environment is collected. As a consequence, an accurate channel dataset for multi-UAV cooperative emergency communications is established, which provides a solid data foundation for unveiling the multi-UAV cooperative channel statistical properties. Based on the constructed dataset, the multi-UAV cooperative emergency communication channel statistical properties are derived and thoroughly simulated, including cooperative time auto-correlation function (TACF), cooperative space cross-correlation function (SCCF), cooperative Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS). According to the derived and simulated multi-UAV co-operative emergency communication channel statistical properties, some conclusions can be drawn. Specifically, compared to scenarios without earthquakes, cooperative SCCFs between the channels related to different antennas in earthquake scenarios are smaller. Channels under earthquake scenarios exhibit smaller SVS than the scenarios without earthquakes.
In this paper, the propagation characteristics of unmanned aerial vehicles (UAVs) to ground under different frequency bands and scenarios are investigated. A new UAV-to-ground communication dataset that covers vari-ous conditions is constructed. We focus on the sub-6 GHz, 15 GHz, and 28 GHz frequency bands under urban and suburban scenarios with different vehicular traffic densities (VTDs) and UAV heights. Furthermore, we use ray-tracing technology to get high-precision channel impulse response (CIR) with Wireless InSite software. The CIR provides detailed insights into the propagation characteristics of UAV-to-ground communication channels. Some important channel statistical properties, such as space cross-correlation function (CCF), time au-to-correlation function (ACF), and Doppler power spectral density (PSD) are derived. The derived statistical properties play a crucial role in the understanding of wireless channels under different conditions. The UAV channel characteristics under multiple-frequency (multi-frequency) multiple-scenario (multi-scenario) conditions are adequately analyzed and valuable conclusions are further obtained. The simulation results demonstrate that frequency bands, VTD, and UAV heights have distinct impacts on UAV channel statistical properties.
In this paper, a real-time unmanned aerial vehicle (UAV)-to-ground path loss prediction model via intelligent multi-modal sensing-communication integration is developed in the operational mode of Synesthesia of Machine (SoM). In the modeling process, a dataset including multi-modal sensing and communication data is constructed in AirSim and Wireless InSite to support the exploration of the non-linear mapping relationship between physical environment and electromagnetic space. To explore the mapping relationship between the environmental features extracted from multi-modal sensing image data in physical environment and path loss in electromagnetic space, a convolution neural network (CNN) is constructed and trained. Therefore, based on the dataset, the real-time path loss prediction in the UAV-to-ground scenario is achieved. Simulation results show that the prediction average mean square error (MSE) of the proposed model is 6.4641 × 10-5 in the test set. The accuracy and utility of the proposed model are validated by comparing the prediction results of the model and ray-tracing (RT)-based results.
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