## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs
#### 3.5.1 Coverage
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Core Objectives** & **Ref.** & **Method** & **Contribution** \\ \hline \multirow{8}{*}{Coverage} & [127] & UDA-DD UDA-DS & This work proposes UAV-BS algorithms that simplify 3D positioning for minimal UAV-BS placement, enhancing service connections based on user demand and QoS with UDA-DD and UDA-DS algorithms. \\ \cline{2-4}  & [128] & EC & This work optimizes UAV coverage by adjusting antenna parameters to minimize path loss for cell-edge users using an elliptical clustering (EC) algorithm. \\ \cline{2-4}  & [129] & Lloyd-like algorithm & This work achieves reliable wireless uplink for users by optimizing the 3D deployment of drones as base stations, while minimizing user transmission power consumption. \\ \cline{2-4}  & [130] & ADPM & This work introduces an alternating-direction penalty method (ADPM)-based iterative algorithm to optimize UAV deployment and antenna orientations in multi-UAV systems for improved sensing accuracy. \\ \cline{2-4}  & [131] & Joint optimization & This work proposes a dynamic model and offloading scheme for UAV-BS systems to reduce resource waste and interference by optimizing the serving radius (SR), UAV deployment, and coverage. \\ \hline \multirow{8}{*}{Anti-interference} & [132] & Joint optimization & This work presents an energy-efficient deployment algorithm for UAVs as flying roadside units (RSUs) in vehicular networks, considering power consumption, latency, and backhaul link capacity for smart city V2X applications. \\ \cline{2-4}  & [133] & Altitude optimization & This work develops a 3D multi-UAV deployment algorithm to maximize ground user system throughput under co-channel interference, using a two-stage approach for QoS-driven UAV network deployment. \\ \cline{2-4}  & [134] & K-means clustering & This work proposes a clustering-based algorithm for UAV deployment in 6G networks, addressing UAV mobility-induced interference and QoS issues, with optimized 3D placement and re-clustering for load balancing. \\ \cline{2-4}  & [135] & DRL & This work introduces a QoE-driven, energy-efficient adaptive deployment strategy for multi-UAV networks using hybrid DRL, optimizing UAV mobility, energy consumption, and throughput. \\ \hline \multirow{8}{*}{Multi-UAV deployment} & [136] & AAP placement & This work presents an energy-efficient 3D placement algorithm for aerial access points (AAPs) to optimize uplink communication, addressing inter-cell interference and energy consumption. \\ \cline{2-4}  & [137] & DLA & This work introduces a decentralized learning algorithm (DLA) for optimizing the 3D deployment of UAV swarms in MIMO systems to maximize channel capacity. \\ \cline{2-4}  & [138] & WVP & This work proposes a decentralized deployment algorithm based on Weighted Voronoi Partition (WVP) for UAV-based flying base stations to minimize UAV-user distance while maintaining connectivity with stationary base stations. \\ \hline \multirow{8}{*}{Energy-saving deployment} & [139] & K-CUT & A heuristic algorithm optimizes the AAP position and remodels the channel allocation problem as an interference minimization problem, using a K-CUT-based algorithm for A2G networks. \\ \cline{2-4}  & [140] & GS & Energy-efficiency and task completion time are enhanced by optimizing the transmission power and flight trajectory, using a user grouping strategy (GS) based on the traveling salesman problem (TSP). \\ \cline{2-4}  & [141] & Joint optimization & This study addresses the energy-saving issue of deploying UAV swarms to fully cover target areas under cooperation and no-fly zone constraints. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Significant research on the deployment of UAVs. et al. [130] designed a joint optimization approach for multi-UAV deployment and directional antenna orientation. Furthermore, the service radius (SR) of UAVs is not fixed during the tracking process. [PERSON] et al. [131] proposed a dynamically adjustable model to optimize UAV deployment.