## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.4 SDN/NFV Technology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Technologies** & **Ref.** & **Method** & **Contribution** \\ \hline Energy harvesting & [115] & OAA & A multi-criteria framework for energy-efficient RRM in IoT networks is developed, proposing an outer approximation algorithm (OAA) to solve the joint optimization problem for device selection, drone relay allocation, and power allocation. \\ \cline{3-5}  & Energy harvesting & [116] & Convex optimization & The article proposes an offline convex optimization method and an online convex-assisted reinforcement learning approach to jointly design UAV flight trajectories, user association, and power control for solar-powered WNs. \\ \cline{3-5}  & Energy-saving data transmission & [117] & PSO CGA & The study examines primary network interruption probability, secondary network throughput, and EAV leakage probability, proposing a hybrid particle swarm optimization and continuous genetic algorithm (PSO-CGA) approach to optimize UAV relay (UR) configuration and NOMA power allocation. \\ \cline{3-5}  & Energy-saving data transmission & [118] & Encoding cache & An encoding caching strategy is proposed to reduce GEO and UAV backhaul traffic, saving transmission energy by considering user heterogeneity and channel models. \\ \cline{3-5}  & Energy-saving data transmission & [119] & Q-Learning DQN & The study presents Q-learning and DQN algorithms to enhance energy efficiency in hybrid UAV-assisted networks, choosing optimal terms tech for UAV-GS links, surpassing standard routing and hybrid schemes in energy use and latency. \\ \cline{3-5}  & Energy-saving task scheduling & [120] & SCA & The study minimizes maximum UAV energy consumption, solving the non-convex mixed-integer nonlinear programming problem with successive convex approximation-based algorithms. \\ \cline{3-5}  & [121] & Game theory & A two-tier offloading strategy reduces UAV-mounted edge device energy consumption through UAV mesh topology connections, using game-theoretic mechanisms for optimal offloading decisions. \\ \hline \hline \multirow{9}{*}{SDN and NFV Technology} & [123] & Heuristic method & This study enhances SAGIN slicing resource allocation with the Slice-Soft-SAGIN framework, jointly optimizing wireless and wired resources for efficient service orchestration through detailed module design and evaluation. \\ \cline{3-5}  & [122] & GA & A k-means-based network partitioning strategy minimizes latency between switches and controllers, dynamically adjusting subnet management using controller load factors and GA-based allocation. \\ \cline{3-5}  & [124] & DRL & The resource scheduling problem is modeled as a multi-domain VNE problem, optimized with DRL by constructing feature matrices and training agents to derive node embedding probabilities. \\ \cline{3-5}  & Technology & Gale-Shapley & Using a reconfigurable time-expanded graph (RTEG), a multi-slot matching game algorithm combined with [PERSON]’s algorithm efficiently identifies the shortest paths. \\ \cline{3-5}  & [23] & DSAC & A heterogeneous federated learning framework with UAV edge servers and LEoS satellite cloud servers addresses task fairness within limited satellite service time, leveraging UAV trajectory planning, DSAC algorithms, and dynamic reward adjustment. \\ \hline \multirow{2}{*}{[126]} & Heuristic and RL hybrid method & The VNE problem incorporates link availability, processing time, and data rate demands, solved via a linear relaxation-based heuristic algorithm and an RL-VNE solution adaptable to network changes. \\ \cline{3-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._cally adjusting the bandwidth and computing resources of each slice, thereby improving resource utilization. [PERSON] et al. [123] proposed a framework called Slice-Soft-SAGIN, which ensures service integrity through sequential deployment while optimizing spectrum and computing resources jointly to enhance orchestration efficiency.