## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.4 SDN/NFV Technology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Key Technologies** & **Ref.** & **Method** & **Contribution** \\ \hline \multirow{8}{*}{Time consumption} & \multirow{3}{*}{[102]} & RTS-RA & The work introduces an access and transmission control scheme with a robust task scheduling with risk aversion (RTS-RA) algorithm for offloading tasks, outperforming others in delay, data transmission, and energy consumption. \\ \cline{2-4}  & \multirow{3}{*}{[103]} & RS-DRL & The study proposes a delay-oriented task scheduling (DOTS) scheme for SAGINs, using risk-sensitive (RS) DRL to minimize UAV processing delays under energy constraints. \\ \cline{2-4}  & \multirow{3}{*}{[104]} & DDPG & The study minimizes IoT device processing delays in SAGINs by optimizing task scheduling and UAV trajectories. \\ \cline{2-4}  & \multirow{3}{*}{[105]} & CMDP & UAVs are deployed as “flying gateways” to collect tasks, using a constrained MDP (CMDP) model to minimize system delay while considering task drop rates and energy consumption. \\ \hline \multirow{8}{*}{Task scheduling} & \multirow{3}{*}{[68]} & \multirow{3}{*}{TD3} & A DRL-based twin-delayed deep deterministic policy gradient (TD3) algorithm optimizes UAV trajectories in SAGINs under jamming attacks, maximizing energy efficiency and user fairness. \\ \cline{2-4}  & \multirow{3}{*}{[106]} & Swap Matching SCA & The study solves the system cost minimization problem for task scheduling and multi-UAV deployment with exchange matching and convex approximation algorithms. \\ \cline{2-4}  & \multirow{3}{*}{[29]} & DQN & A decentralized computing network task scheduling model is proposed, minimizing computing and transmission costs while ensuring queue stability through a two-stage optimization algorithm. \\ \cline{2-4}  & \multirow{3}{*}{[107]} & MADDPG & A three-dimensional multi-UAV-assisted MEC system is proposed, considering fairness and security, using the Nash equilibrium and multi-agent deep deterministic policy gradient (MADDPG) algorithms for optimization. \\ \hline \multirow{8}{*}{[108]} & \multirow{3}{*}{GA} & A task priority model is proposed, developing a computation offloading framework that includes user grouping and joint task offloading and UAV scheduling, solved with a genetic algorithm (GA). \\ \cline{2-4}  & \multirow{3}{*}{[109]} & DRL & A general task scheduling framework is constructed for diverse task types and requirements in SAGINs. \\ \hline \multirow{8}{*}{[110]} & SAC & The study proposes a hybrid multi-agent soft actor-critic (SAC) algorithm for joint task offloading and resource allocation in MEC. \\ \cline{2-4}  & \multirow{3}{*}{[111]} & RSU-UAV collaboration & A proposed strategy for drone and roadside unit (RSU) resource allocation uses probabilistic access, dual-base station control, and load balancing to enhance vehicular network performance, enabling dynamic power management and efficient service delivery. \\ \cline{2-4}  & \multirow{3}{*}{[33]} & 3D-2D-MM & A 3D-2D Markov Model (3D-2D-MM) for UAV satellite communication is proposed, with a 3D dynamic turbo approximate message-passing (3D-DTAMP) algorithm for tracking dynamic channels. \\ \cline{2-4}  & \multirow{3}{*}{[113]} & \multirow{3}{*}{SAGE} & A Space-Alternating Generalized Expectation–Maximization (SAGE) and K-Power-Means (KPM) method combined with a clustering-based tracking (CBT) approach models time-varying A2G channels, enhancing UAV wireless communication system modeling. \\ \hline \multirow{8}{*}{[114]} & \multirow{3}{*}{\begin{tabular}{c} Primal decomposition \\ \end{tabular} } & Primal decomposition with a gradient projection iterative algorithm optimizes energy efficiency in UAV computation offloading, addressing UAV mobility, A2G communication, and computation dynamics. \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{\
\begin{tabular}{c} Primal decomposition \\ \end{tabular} } & Primal decomposition with a gradient projection iterative algorithm optimizes energy efficiency in UAV computation offloading, addressing UAV mobility, A2G communication, and computation dynamics. \\ \cline{1-1} \cline{2-4}  & &
\begin{tabular}{c} Primal decomposition \\ \end{tabular} & Primal decomposition with a gradient projection iterative algorithm optimizes energy efficiency in UAV computation offloading, addressing UAV mobility, A2G communication, and computation dynamics. \\ \hline \hline \end{tabular}
\end{table}