# OTFS-Based Handover Triggering in UAV Networks
## 3 UAV to Ground Handover Scenarios and Channel Modeling  
Figure 1 illustrates three dominant handover scenarios within the UAV-to-ground communication network, most of these scenarios are given in [24]. In the first scenario, given in Figure 1a, the airplane or fixed-wing UAV remains connected to GBSs for navigation and control. Along its predetermined path, it should switch among different GBSsto maintain continuous connectivity. The second and third scenarios given in Figure 1b,c involve a swarm of UAVs working as ABSs deployed to cover vehicles and HSTs, where vehicles/HSTs travel at high speeds. The HST requires handovers between UAVs to relay its data to the closest GBS, ensuring continuous communication, especially in challenging regions like rural and mountainous areas, where installing multiple GBSs is costly. Here, seamless communication requires vehicles/HSTs to transition between UAVs as they move along the highway/railway. The primary factors influencing these different UAV-to-ground handover scenarios are the exceptionally high velocities of fixed-wing UAVs, highway vehicles, and HSTs. These velocities vary from several Km/Hr for drones to several hundred or even one thousand Km/Hr for fixed-wing airplane/UAVs or HSTs. In such high-speed scenarios, the time-domain channel experiences significant fluctuations, rendering the utilization of RSS or even FT channel gains for handover triggering inefficient. These schemes would lead to an excessive number of unnecessary handovers and subsequently a lower throughput. Therefore, a novel handover-triggering mechanism should be developed to address the challenges of high-mobility environments, ensuring stable handover performance regardless of the high velocities of both UAV and ground entities. For UAV-to-ground DD channel modeling, we followed the time-varying DD-channel-spreading function (CSF) given in [8; 25]. In this modeling, small-scale fading results from the time-varying channel path gains, fractional Doppler shifts and fractional delay times of the physical DD channel are considered. Towards that, the discrete DD channel impulse response (CIR) is divided into \(\text{Q}=N/n^{\prime}\) several coherence periods in the DD grid, where \(n^{\prime}T\) s is the duration of each coherence period, where \(NT\) s is the total duration of the OTFS symbol. The OTFS symbol occupies a total bandwidth of \(M\Delta f\) Hz, where \(\Delta f\) is the subcarrier spacing and \(\Delta fT=1\). Based on [8], the CIR of the \(q\)-th coherence period, \(1\leq q\leq\text{Q}\) can be written as follows:  
\[h_{q_{DD}}(\alpha,\beta)=\sum_{i_{q}=1}^{p_{q}}h_{i_{q}}\delta\left(\alpha- \alpha_{i_{q}}\right)\delta\left(\beta-\beta_{i_{q}}\right), \tag{1}\]  
where \(P_{q}\) is the number of paths (each of which is composed of inseparable paths) in the \(q\)-th coherence period. Also, \(h_{i_{q}}\), \(\alpha_{i_{q}}\), and \(\beta_{i_{q}}\) are the complex amplitude, the delay spread, and the Doppler shift in path \(i_{q}\). Then, the whole quantized DD CIR resulting from the Q coherence periods becomes the following [8]:  
\[h_{DD}\left[\frac{l}{M\Delta f^{\prime}},\frac{k}{NT}\right]=\frac{1}{MN}{\sum _{q=1}^{Q}}{\sum_{i_{q}=1}^{P_{q}}h_{i_{q}}e^{-2\pi\alpha_{i_{q}}\beta_{i_{q} }}\mathcal{A}\left(i_{q},\beta_{i_{q}},k\right)\mathcal{B}\left(i_{q},\beta_{ i_{q}},l\right)} \tag{2}\]  
Figure 1: Handover scenarios in aerial-to-ground network: (**a**) A fixed-wing airplane switches among different GBSs to maintain continuous connectivity. (**b**) A car in the roadway switches between multiple UAVs working as ABSs for seamless communication. (**c**) An HST switches between multiple UAVs working as ABSs for relaying information and providing continuous wireless coverage in hard-to-reach areas.