# OTFS-Based Handover Triggering in UAV Networks
## 2 Related Works  
Several research studies have investigated the integration of UAV systems with ground networks, recognizing their unique flight capabilities within the broader framework of SAGIN [4, 5, 6, 7]. In [4], a UAV is used for post-disaster rescue operations, where online learning was utilized to optimize its trajectory for maximizing the total system rate while considering the limited battery budget of both UAV and victims. In [5], the authors explored how UAVs serving as ABSs can enhance content delivery for metaverse users, focusing on factors like delivery success probability and average delay. It proposed a novel ABS association strategy based on received power and cache hit probability, highlighting optimal UAV deployment parameters for improved content delivery in the metaverse. In [6], UAV mounted RIS was used to strengthen the coverage in crowdy hotspots, such as stadiums or train stations. In this regard, the authors proposed an online learning approach to optimize the UAV trajectory over these hotspots without any prior information about them or their traffic needs. In [7], a laser-powered UAV-based data collection scheme for IoT networks was proposed to enhance energy efficiency, introducing an improved clustering algorithm and a novel approach for determining optimal visiting order and entry points for IoT device clusters.  
Regarding UAV-to-ground handovers, the primary focus of this paper, there exists some research efforts exploring this issue, as given in [9, 20, 21, 22, 23]. In [9], a path-loss-plus-fading model for handover in UAV networks was proposed, where a discrete time Markov chain using Nakagami-\(m\) distribution was utilized to analyze the handover-triggering probability. However, this study relies on the traditional RSS-based handover-triggering method. In [20], deep reinforcement learning (DRL) was used to enhance connectivity for UAVs within cellular networks by improving the handover routines. Yet, RSS is still used for handover triggering. In [21], the software-defined networking (SDN) platform was enhanced by using UAVs as on-demand forwarding switches. The aim was to reduce handover latency and improve the handover performance of SDN using UAVs, but still RSS was the main enabler for handover triggering. In [22], the impact of different altitudes of UAVs on handover performance was investigated via addressing a gap in understanding 3D placement effects. Through modeling and analysis, the study establishes equivalence in handover occurrence processes across mobility scenarios and derives computationally efficient upper bounds for handover rates, demonstrating their accuracy through numerical examples. However, RSS was used as the metric for handover triggering. In [23], an overview of handover management challenges for drones within future mobile networks was given through highlighting the complexity posed by their three-dimensional mobility. The handover schemes considered both cases when drones are used as ABS or as users for the ground network. It synthesized current research approaches and discussed potential solutions for integrating drones into heterogeneous networks, providing insights into future research directions in this domain. However, all handovers' schemes surveyed by this paper relied on RSS as the main trigger for handovers.  
To the best of our knowledge, this work is the first proposal that utilizes the estimated DD channel Top of Form, employing OTFS modulation to trigger handover decisions in the UAV-to-ground networks. Instead, all existing research works relied on the traditional RSS for triggering these handovers, which is unsuitable for these high-speed environments.