## 3 Communication and Channel Modeling
### Channel Modeling
#### 3.1.4. Obstructed LoS and Outage Probability  
Channel modeling in UAV-enabled communication is mostly based on Rician fading and free path loss gain for compromising LoS links and scattering [47]. Rician distribution is a more realistic scenario since the UAV is in a high position in the sky and can create a direct LoS to UEs. However, since future RIS-assisted mmWave communication networks most likely will be limited to beamforming to LoS paths, deterministic LoS channel gain has been considered [48]. Also, probabilistic LoS is common in urban environments with buildings appearing as random obstacles. This issue was initially considered for coverage maximization of low-altitude aerial platforms (LAPs) [49]. Following this work, some papers have used the developed probabilistic LoS modeling for applications in urban environments [50; 51]. The probability of LoS link between the UAV and ground UE in urban areas is obtained as follows:  
\[\mathrm{Prob}_{\text{LoS}}=\frac{1}{1+a\ \exp\left(-\mathrm{b}\left(\frac{ \dfrac{180}{\pi}\mathrm{arcsin}\bigg{(}\frac{z^{UAV}}{d^{UAV\_UE}}\bigg{)}}{z ^{UAV\_UE}}\right)\right)-a} \tag{22}\]  
where \(a\) and \(\mathrm{b}\) are constant values determined based on the environment; \(z^{UAV}\) denotes the UAV altitude; \(d^{UAV\_UE}\) denotes the Euclidean distance; \(\zeta^{UAV\_UE}\) denotes the elevation angle between UAV and UE. The outage probability is \(1-\mathrm{Prob}_{\text{LoS}}\). Therefore, since the elevation angle in the deterministic LoS impact the channel, there is a tradeoff between elevation angle and distance that is considered in the RISeUAV (or UAV-BS) placement and path planning [52].  
Further, more recently, the stochastic geometry of the LoS path has been considered in determining the LoS probability of the A2G links [53]. In the case of RISeUAV, it is assumed the LoS links are valid in the incident and reflection directions, thanks to the great mobility of the UAV [48]. Therefore, a deterministic CLoS channel model has been considered as given in (9). However, the channel model is affected by a variety of realistic physical disturbances affecting the channel conditions such as the UAV mobility [54] and weather conditions [55]. By incorporating the UAV's rotational aerodynamics, the array response of the RIS array in the RISeUAV has been modified following UAV fluctuations [54]. The environmental characteristics affect the small-scale and large-scale fading and potential spatial correlation in shadowing. Therefore, to realize a generic mode, (9) is modified through a statistical study to develop a generic model [55], where the closed-form expressions for the outage probability and the ergodic capacity have been presented.  
Also, the RISeUAV faces the double-Rician channel, due to the CLoS link that needs a new closed-form for modeling the outage probability [56]. The impact of the UAV position and the number of RIS reflective surface elements on the channel modeling and the performance of the communication network are investigated using Monte Carlo simulations [56]. Further, the variance of the double-Rician channel and a closed-form expression of the outage probability have been developed.  
Realistic environment testing has characterized obstructed V2V LoS links, highlighting the impact of shadowing from other vehicles and self-shadowing on flat and sloped terrains [57]. Higher antenna placement (e.g., on vehicle roofs) can improve coverage in obstructed scenarios but may worsen coverage on sloped terrains due to self-shadowing. Deploying RISeUAV can effectively provide aerial indirect LoS links to enhance V2V communication.