# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area
### Discussion  
The field trial successfully demonstrated the feasibility of NTN in support of UAIS in a mountainous area with no preexisting communication network infrastructure. However, there are still technological challenges with the UAV NTN network for providing coverage in remote mountainous areas, which will be discussed below from different perspectives. Firstly, intelligent service switching and network transmission coding optimization are required to address the issue of service interruption caused by dynamic changes in the UAV posture in complex on-site network environments. When multiple UAVs carrying 5G base stations transverse various terrains, user terminals will frequently switch between UAV 5G base stations, resulting in service interruptions and poor quality of service (QoS) in this scenario. The handover paradigm between base stations is of great importance, and further effort is required to investigate this issue. The application of edge computing server onboard the UAV enables the perception of connection status, facilitating intelligent prediction and active planning of the base station handover paradigm. This ensures the continuity of the command and dispatching service during the base station handover. A network acceleration device utilizing forward error correction coding has been designed and implemented in a 5G multi-access environment. This device can simultaneously utilize multiple available communication links in a complex network environments to achieve efficient data transmission. In weak network environments with fluctuating wireless links, reducing the end-to-end packet loss rate and increasing the rate of multi-link aggregation transmission can address the challenges of poor reliability and the difficulty in ensuring stable services in mountainous communication environments. Secondly, real-time computation offloading technology based on optimal transmission theory is urgently needed to solve the real-time response problem of unmanned aerial vehicles. Using the new technology of edge computing, different services with different requirements for computing resources and time limitations can be satisfied. In addition, global situation awareness needs to rely on the high-performance computing power of the rear cloud computing center, integrate image data, weather, and other multi-modal data for centralized computing, while control and dispatching applications with high real-time requirements such as trajectory planning need to be accomplished with low latency is processed on edge computing nodes. An intelligent task offloading algorithm that integrates optimal transmission theory and unmanned aerial vehicle control needs in-depth investigation. Considering the capabilities of the front and rear computing nodes and network bandwidth, the strategy can perform intelligent allocation and dynamic scheduling of computing tasks, maximizing the utilization of on-site computing and communication resources, improving data processing efficiency, and reducing the communication service response latency. Finally, the field trial has revealed the problem of a lack of UAV online trajectory adjustment capabilities according to user QoS. In the field trial test, the trajectory of the UAV is controlled manually by human operators in the control center. The operator has no idea of user-end QoS status; therefore, they are unable to adjust the trajectory to enhance the performance of the user equipment. To handle this issue, an intelligent UAV trajectory control scheme considering user equipment QoS and channel status would benefit overall system performance, which is vital for improving users' experience, especially at the cell