###### Abstract
Measurements of an AG communication channel, at 2.585 GHz and 3.5 GHz, based on different trajectories of UAVs, were conducted in hilly scenarios [18], where shadow fading (SF), path loss (PL), root mean square (RMS) delay spread (DS), and other characteristics of the UAV AG channel were analyzed and compared to the measured data. Measurements of the AG channel were conducted at C-band and L-band, in multiple scenarios, in [19; 20; 21], including near-urban, hilly, over-water, suburban, and mountainous environments. Through the measured data, the most direct AG statistical properties of the different terrains were obtained. Moreover, the effect of airframe shadowing on the AG channel was further investigated, and the method of deploying multiple antennas to help mitigate this effect was validated [22]. In [23], AG channel measurements were conducted at multiple frequencies, and then a novel shadow fading model was proposed. The relevant small-scale fading characteristics were also verified by the measurement data. Channel measurements in some specific scenarios are also ongoing. Measurements were conducted in a linear subway tunnel at 1.8 and 5.8 GHz, resulting in some large-scale channel characteristics [24]. Measurements for vehicle-to-vehicle (V2V), at 5.9 GHz, based on street intersection scenarios, were carried out, and some measurement data were provided [25]. Although measurement data can provide first-hand channel characteristics, some measurement environments are complex, and the measurement cost is high; therefore, using a deterministic or stochastic channel model of simulation data can more easily simulate the UAV AG communication channel. A general 5G channel model for small-scale fading characteristics, based on the WINNER II and Saleh-Valenzuela (SV) channel models, has been proposed. Moreover, the cluster evolution on the time axis has been investigated [26]. The study of small-scale fading is extremely important for UAV AG communication channels. A GBSM with 3D dynamic trajectories at the transmitter (Tx) and the fixed receiver (Rx) was studied in article [27]. Some important channel characteristics, such as power delay profile (PDP), stationary interval, space-time correlation function (STCF), Doppler PSD, RMS DS, and RMS Doppler spread, were analyzed. Taking into consideration level crossing rate (LCR), STCF, Doppler PSD, and average fade duration (AFD), article [28] proposed a regular-shaped, geometry-based statistical model (RS-GBSM) for a UAV MIMO channel. The statistical properties of the channel were derived, based on the effects of parameters such as flight altitude and direction of movement. A model with mixed bouncing was proposed, to capture AG channel characteristics [29]. A general channel model framework, which was suitable for multi-frequencies and multi-scenarios, was proposed [30]. Taking into consideration the changing moving direction and speed of the MS, a channel model of 3D wideband MIMO was proposed [31], and the statistical properties were verified by simulation data and theoretical data. In order to capture the effects of objects and human movement, a 3D channel model for a moving point scatterer in indoor scenarios was proposed in paper [32]. To obtain the real-time azimuth angle of departure (AAoD), the elevation angle of departure (EAoD), the azimuth angle of arrival (AAoA), and the elevation angle of arrival (EAoA), a novel angle estimation algorithm was presented in a 3D MIMO channel model for AG communications [33]. As far as the authors know, current research on the UAV AG channel model mainly focuses on the channel characteristics of fixed BSs, while research on AG multi-mobility multi-trajectory channel characteristics is still lacking. In some typical UAV-assisted communication scenarios--especially emergency communication scenarios--there is a higher randomness in the trajectory of the Tx and the Rx.