# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Mobile Edge Computing in NTN Communications  
In real-world applications, users such as IoT devices have limited batteries and relatively low computation resources, posing challenges for IoT devices in terms of executing computation-intensive applications. UAVs with onboard computation capabilities are promising in terms of supporting mobile edge computing (MEC) to solve the problem. In UAV-aided non-terrestrial networks, IoT devices with limited commutation resources can offload their computation-intensive tasks to a UAV with abundant computation resources at the edge of the network, resulting in reduced energy consumption and communication overhead to back-end cloud servers. Compared to terrestrial stationary base stations acting as MEC infrastructure, UAV-aided MEC has the potential to serve the users on demand, which also poses challenges to these systems. However, owing to the limited payload capacity and energy, UAVs have limited capabilities when serving as MEC servers. Therefore, MEC systems supported by UAVs have urgent requirements in terms of motion control and energy consumption optimization. Many existing studies utilized a centralized strategy to optimize the offloading process, which is inefficient in multi-UAV/user scenarios. To resolve the computing resource allocation problem in UAV-aided MEC, the authors of [60] proposed a decentralized multi-agent reinforcement learning-based method to minimize computation latency for a number of users. To minimize power consumption in a multi-UAV MEC scenario [61], proposed a multi-layer strategy utilizing UAV deployment location optimization and offloading task prediction based on a long short-term memory mechanism. Considering the complicated UAV-aided MEC system, the optimization of UAV motion control, energy consumption, and computation resources cannot be fulfilled using \begin{table}
\begin{tabular}{c c c c} \hline
**Literature** & **Topics** & **Contributions** & **Year** \\ \hline \multirow{3}{*}{[47]} & \multirow{3}{*}{UAV User Equipment} & The paper considered a DRL-based UAV & \multirow{3}{*}{2022} \\  & & user optimization method considering & \\  & & impact factors of UAV power, flight & \\  & & trajectory, and antenna pattern. & \\ \hline \multirow{3}{*}{[48]} & \multirow{3}{*}{UAV User Equipment} & The paper considered a cellular-connected & \multirow{3}{*}{2022} \\  & & UAV for synthetic aperture radar sensing & \\  & & tasks using successive convex & \\  & & approximation. & \\ \hline \multirow{3}{*}{[49]} & \multirow{3}{*}{UAV User Equipment} & The paper proposed a dynamic & \multirow{3}{*}{2020} \\  & & programming algorithm to optimize & \\  & & trajectories of cellular-connected UAVs for & \\  & & communication enhancement & \\ \hline \multirow{3}{*}{[50]} & \multirow{3}{*}{UAV User Equipment} & The paper reported a path-planning method & \multirow{3}{*}{2022} \\  & & for task-critical missions in which & \\  & & connectivity maintenance is of utmost & \\  & & importance. & \\ \hline \multirow{3}{*}{[51]} & \multirow{3}{*}{UAV User Equipment} & This work reported a UAV curvature design & \multirow{3}{*}{2019} \\  & & for UAV users with fixed start and final & \\  & & locations. & \\ \hline \multirow{3}{*}{[52]} & \multirow{3}{*}{UAV User Equipment} & This study emphasized the role of path & \multirow{3}{*}{2022} \\  & & planning in the effort of interference & \\ \cline{1-1}  & & cancellation. & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._conventional convex methods, which is challenging. For trajectory design of UAV MEC systems [62], proposed a DRL paradigm to control UAV trajectory and maximize the system's stability at the same time. In a multiple-input single-output (MISO) UAV MEC scenario, the authors of [63] studied the energy-saving strategy considering beam-forming and UAV trajectory. Owing to the line-of-sight connections between UAV MEC platforms and ground users, the data offloading task can be achieved at high speed.