# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 3 System Model
### UAV-Assisted Vehicular Channel Model  
The task of utilizing UAVs as intermediate relay bridges to facilitate the construction of VANET networks for enabling communication between two vehicles that are not within direct range of each other has been extensively studied in previous research papers. However, this specific scenario is not the focus of this paper. Instead, this paper primarily focuses on studying the connections formed within the communication range of a UAV. Each UAV categorizes and prioritizes all vehicles within its coverage area based on their speeds. By calculating the total delay, performing comparisons, and making optimal allocations, this paper aims to provide a detailed explanation. The relationship between drones and vehicles' positions is as shown in Figure 2.  
The speed \(v_{f}\) of each vehicle is gathered at location \(x_{0}\) and remains constant until it exits the communication range of the UAV. At any given time, a vehicle establishes an uplink connection with the UAV. To ensure that the UAV can receive signals from each vehicle and obtain signal strength, this model is considered to be composed of both LoS and NLoS links [22]. Depending on the UAV's position and flight altitude, these links will have different probabilities. The vehicle K's position is defined as \([x_{k},y_{k},h_{k}]^{T}\in R^{371},k\in K\), while the UAV's position is \([x_{u},y_{u},h_{u}]^{T}\in R^{371},u\in U\), and \(h_{u}\) must be optimized to satisfy QoS constraints. As shown in Figure 2, according to the Euclidean distance, the distance d between vehicle k and UAV u can be written as:  
\[d_{uk}=\sqrt{\left(x_{u}-x_{k}\right)^{2}+\left(y_{u}-y_{k}\right)^{2}+\left( h_{u}-h_{k}\right)^{2}}. \tag{4}\]  
Assuming \(h_{k}\) negligible values are ignored and considered as 0, the probabilities of LoS and NLoS links corresponding to the UAV are as follows:  
\[p_{uk}^{LoS}=\frac{1}{1+ae^{-b\left(\frac{180}{\pi}\theta-a\right)}}, \tag{5}\]  
\[p_{uk}^{NLoS}=1-p_{uk}^{LoS}, \tag{6}\]  
Figure 2: Vehicle–UAV Position Relationship Diagram.  
where \(a\) and \(b\) are constants determined by the environment and carrier frequency, \(\theta_{\rm{uk}}=\arctan^{\rm{h_{u}}}/r_{\rm{uk}}\) is the elevation angle, and \(r_{\rm{uk}}\)=\(\sqrt{\left(x_{\rm{u}}-x_{\rm{k}}\right)^{2}+\left(y_{\rm{u}}-y_{\rm{k}}\right)^{2}}\). Thus, the path loss expressions for LoS and NLoS links can be derived as follows:  
\[PL_{\rm{uk}}^{LoS}(dB)=10n_{a}log(\frac{4\pi f_{c}d_{uk}}{c})+\eta_{LoS}, \tag{7}\]  
\[PL_{\rm{uk}}^{NLoS}(dB)=10n_{a}log(\frac{4\pi f_{c}d_{uk}}{c})+\eta_{NLoS}, \tag{8}\]  
where, \(n_{a}\) represents the path loss exponent, and \(\eta_{LoS}\) and \(\eta_{NLoS}\) are determined by the environment and represent the average additional loss for free space propagation in LoS and NLoS connections, respectively. \(c\) denotes the speed of light, and \(f_{c}\) represents the carrier frequency. Therefore, we can obtain the average path loss and the average channel gain as follows:  
\[PL_{\rm{uk}}=p_{uk}^{LoS}PL_{\rm{uk}}^{LoS}+p_{uk}^{NLoS}PL_{\rm{uk}}^{NLoS}, \tag{9}\]  
\[G_{uk}=\
icefrac{{1}}{{p}}{L_{\rm{uk}}}. \tag{10}\]  
Thus, we can derive the SNR received by the UAV u from the vehicle at its current position as follows:  
\[SINR_{uk}=\frac{P_{k}G_{uk}}{(I_{C}+N_{0})W}\, \tag{11}\]  
where \(P_{k}\) is the transmission power of vehicle k, \(I_{C}\) represents co-channel interference, \(N_{0}\) denotes Gaussian white noise, and W is the bandwidth. According to the model, to achieve effective data transmission between the UAV and the vehicles, it is necessary to maximize the end-to-end rate while minimizing latency and power consumption.