## 3 System Model and Problem Formulation
### Task Computation Model  
To reduce collisions, we take the request to send/clear to send (RTS/CTS) method into account [58]. As shown in Figure 3, before sending the task, the vehicle \(i\) needs to send an RTS control frame to request channel occupancy. Then, if the wireless channel is idle, UAV \(j\) can receive the RTS frame and reply with a CTS frame after the time interval SIFS. Afterward, vehicle \(i\) receives the CTS frame and starts sending the task after the time interval SIFS. Finally, UAV \(j\) responds with an ACK frame to vehicle \(i\) after receiving the task.  
Therefore, we define the successful transmission task \(\Xi_{i}\) period between vehicle \(i\) and UAV \(j\) as \(T_{i,j}^{s}\), such that  
\[T_{i,j}^{s}=T_{i,j}^{t}+H_{\Xi_{i}}+4T_{i,j}^{p}+3T_{SIFS}+T_{ACK}+T_{DIFS}+T_{ RTS}+T_{CTS}, \tag{5}\]  
Figure 3: Request to send/clear to send (RTS/CTS) method.  
where \(H_{\Xi_{i}}\) represents the task \(\Xi_{i}\) overhead of packet header, and \(T_{i,j}^{p}\) represents the propagation delay. \(T_{SIFS}\), \(T_{ACK}\), \(T_{DIFS}\), \(T_{RTS}\), and \(T_{CTS}\) represent the SIFS frame interval, the ACK frame interval, the DIFS frame interval, the RTS frame interval, and the CTS frame interval, respectively. In addition, \(T_{i,j}^{t}\) represents the transmission delay, which can be written as  
\[T_{i,j}^{t}\approx\frac{\lambda_{i,j}d_{i}}{B_{j}\text{log}_{2}\bigg{(}1+ \frac{P_{i}^{\Xi_{i}}G_{i,j}}{P_{n}}\bigg{)}}, \tag{6}\]  
where \(B_{j}\) represents the bandwidth of UAV \(j\), \(P_{i}^{\Xi_{i}}\) represents the transmission power of vehicle \(i\), \(P_{n}\) represents the noise power at the receiver input, and \(G_{i,j}\) represents the channel gain between vehicle \(i\) and UAV \(j\).  
Let \(T_{i,j}^{c}\) denote the wireless channel collision period, which can be expressed as  
\[T_{i,j}^{c}=T_{RTS}+T_{DIFS}+T_{i,j}^{p}. \tag{7}\]  
Therefore, the normalized throughput \(H_{i,j}\) can be calculated as  
\[H_{i,j}=\frac{p_{i,j}^{s}\lambda_{i,j}d_{i}}{p_{i,j}^{f}\delta+p_{i,j}^{s}T_{ i,j}^{s}+p_{i,j}^{c}T_{i,j}^{c}}, \tag{8}\]  
where \(\delta\) represents the duration of a timeslot.  
According to (5)-(8), the transmission time \(T_{i,j}^{trans}\) of task \(\Xi_{i}\) from vehicle \(i\) offloading \(\lambda_{i,j}d_{i}\) to UAV \(j\) is given by  
\[T_{i,j}^{trans}=\frac{\lambda_{i,j}d_{i}}{H_{i,j}}=\frac{p_{i,j}^{f}\delta+p_{ i,j}^{s}T_{i,j}^{s}+p_{i,j}^{c}T_{i,j}^{c}}{p_{i,j}^{s}}=T_{i,j}^{s}+\frac{p_{ i,j}^{f}\delta+p_{i,j}^{c}T_{i,j}^{c}}{p_{i,j}^{s}}. \tag{9}\]  
For the local computing task \(\big{(}1-\lambda_{i,j}\big{)}d_{i}\) of vehicle \(i\), the local computation time \(T_{i,j}^{loc}\) can be calculated as  
\[T_{i,j}^{loc}=\big{(}1-\lambda_{i,j}\big{)}\frac{c_{i}}{f_{i}}, \tag{10}\]  
where \(f_{i}\) represents the local computing frequency of vehicle \(i\) (GHz/s).  
The computing resources of UAV \(j\) are limited, so the limited computing resources need to be divided into \(N\) vehicles as evenly as possible to ensure that the task can be completed on time. Let \(F_{j}\) denote the total computing frequency of UAV \(j\) and \(f_{i,j}\) represent the computing frequency assigned to vehicle \(i\) by UAV \(j\), i.e., \(\mathbf{f}=\big{\{}f_{1,j},f_{2,j},...,f_{N,j}\big{\}}\). We have \(\sum_{i=1}^{N}x_{i,j}f_{i,j}\leq F_{j}\). The computing time \(T_{i,j}^{Edge}\) of MEC server on UAV \(j\) can be expressed as  
\[T_{i,j}^{Edge}=\frac{\lambda_{i,j}c_{i}}{f_{i,j}}. \tag{11}\]  
As the task \(\Xi_{i}\) can be executed in parallel on vehicle \(i\) and UAV \(j\), the task \(\Xi_{i}\) processing delay \(T_{\Xi_{i}}\) can be expressed as  
\[T_{\Xi_{i}}=\sum_{j=1}^{M}x_{i,j}\max\Big{\{}T_{i,j}^{loc},T_{i,j}^{UAV}\Big{\}}, \tag{12}\]  
where \(T_{i,j}^{UAV}\) represents the processing delay of MEC server on UAV \(j\), \(T_{i,j}^{UAV}=T_{i,j}^{1}+T_{i,j}^{trans}+T_{i,j}^{Edge}\).