## 3 System Model
### Transmission Delay  
Packet transmission delay in UAV networks is influenced by queuing delay \(T_{q}\), channel contention delay \(T_{c}\), and transmission delay \(T_{t}\). Each UAV node maintains a FIFO-based transmission queue. To accurately estimate packet transmission delay, we adopt the M/M/1 queue model, which characterizes a single-server system with Poisson arrivals and exponentially distributed service times, making it well-suited for wireless communication modeling. According to M/M/1 queue theory, the average queuing delay for a packet is  
\[T_{q}=\frac{\rho}{\mu(1-\rho)} \tag{8}\]  
where the queue utilization factor \(\rho=\lambda/\mu\) must satisfy \(0\leq\rho<1\) for stability. The service rate \(\mu\) is determined by the available channel bandwidth \(B\) and the packet size \(L\), such that \(\mu=B(bps)/L(bits)\). UAV nodes typically use the CSMA/CA mechanism for channel access, leading to additional contention delays. We adopt the Bianchi backoff model [42] to characterize this contention. In this model, a node undergoes multiple backoff stages before successfully transmitting a packet, where the backoff time per stage is  
\[T_{\text{backoff}}=\frac{CW_{\text{min}}+1}{2}\cdot T_{\text{slot}} \tag{9}\]  
where \(CW_{\text{min}}\) is the minimum contention window size, and \(T_{\text{slot}}\) is the time slot duration. The expected number of backoff stages, \(E[b]=(1-p_{c})/p_{c}\) (where \(p_{c}\) is the collision probability dependent on network load and neighboring nodes), determines the channel contention delay. \[T_{c}=E[b]\cdot T_{\text{backoff}} \tag{10}\]  
The transmission delay, which represents the time required to physically send the packet over the channel, is given by  
\[T_{t}=\frac{L(bits)}{B(bps)} \tag{11}\]  
Combining the queuing, contention, and transmission delays, the total estimated delay for a packet is  
\[T_{\text{delay}}=\frac{\rho L}{B(1-\rho)}+E[b]\cdot T_{\text{backoff}}+\frac{ L}{B} \tag{12}\]  
Due to dynamic topology changes in UAV networks, packet transmission delay depends on both the current queue state and the future network topology. To enhance routing efficiency, we predict the transmission time and assess whether a suitable next-hop neighbor will be available. For a packet \(p\) arriving at time \(t\), the expected transmission time is  
\[t_{\text{send},p}=t+T_{\text{delay}} \tag{13}\]Using trajectory information shared among the UAV swarm, the set of next-hop neighbors at \(t_{\text{send},p}\) can be determined as:  
\[\mathcal{N}_{i}(t_{\text{send},p})=\{U_{j}\mid d_{ij}(t_{\text{send},p})\leq R\} \tag{14}\]  
where \(R\) is the fixed communication radius.If no next-hop neighbors are available at \(t_{\text{send},p}\),the packet is dropped to prevent unnecessary queuing:  
\[\text{If }\mathcal{N}_{i}(t_{\text{send},p})=\emptyset\text{ then drop packet}. \tag{15}\]  
By incorporating trajectory-based predictions, this approach reduces unnecessary retransmissions, optimizes queue management, and improves overall network efficiency. Figure 2 visualizes how this paper combines the trajectory information with the sending delay model to optimize the queue management of the nodes. In the diagram, dashed blue lines represent the UAVs' planned trajectories, while solid blue arrows denote available communication links. Under a task-driven deployment, UAVs \(U_{A},U_{B},U_{C}\), and \(U_{G}\) converge on the left to execute Task 1, whereas \(U_{D},U_{E}\), and \(U_{F}\) move to the right for Task 2. At time \(t_{k}\), shown in panel (a), both \(U_{A}\) and \(U_{E}\) possess packets in their transmission queues addressed to \(U_{G}\), and their queue lengths are equal. Using the predicted transmission delay, both packets are slated for dispatch at time \(t_{k+5}\), as depicted in panel (b).