# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Path Planning and Control in NTN Communications  
Integrating unmanned aerial vehicles (UAVs) as flying base stations, relays or user equipment is expected to be an important topic in the context of beyond-5G/6G non-terrestrial networks. Multiple operational aspects of UAV-assisted 5G/6G networks have been investigated in various studies, which include UAV place of deployment, ground interference cancellation, antenna orientation optimization and cell partition [30; 31]. From the perspective of unmanned autonomous intelligent systems, the motion control of UAV platforms in communication scenarios is a multi-disciplinary problem that requires knowledge relating to autonomous control, wireless spectrum environment perception, cellular user communication quality awareness, and so on. Thus, trajectory optimization in NTNs has been widely studied. In recent years, UAV trajectory optimization and path planning have drawn extensive attention from research scientists due to the inter-discipline knowledge and sophisticated models involved in the trajectory planning of UAV communication. Efficient trajectory optimization or path planning is conducive to maneuvering according to UAV communication performance and ground user requirements. Although path planning is a basic problem in UAV control, the synergy between UAV path planning and the performance of the wireless communication systems poses new challenges that are not covered by traditional UAV navigation and trajectory optimization paradigms. Deep reinforcement learning (DRL)-based algorithms and other non-convex optimization methods are utilized to solve dynamic path planning and control problems [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. (1) **UAV Path Planning for Flying Base Stations Scenario**  
The deployment position and motion trajectory of UAVs have an impact on the ground communication coverage and capacity when UAVs are used as flying base stations. In general, UAV deployment and trajectory optimization are challenging problems due to the extensive number of parameters involved, such as UAV air-to-ground channel model, inter-UAV interference avoidance, UAV power consumption, motion constraints, and so on. To address the challenges caused by joint optimization for UAV motion control and wireless communication in NTN communications [32], investigated a distributed framework based on DRL to control and optimize UAV base stations when considering ground users' fairness. This work considered flight time as the main constraint to optimize over-all throughput during a fixed flight time. In order to deal with real-time path planning problems for UAV base stations, a variety of research works have been published [33; 34; 35]. In traditional UAV control schemes, ground operating stations have limited knowledge  
Figure 3: Illustration of UAV applications in NTNs. of ground users' status, and thus real-time online trajectory planning is challenging to perform. Moreover [32], considered UAV maneuver curvature for received signal strength optimization in a multi-base station scenario. This problem is challenging since the UAVs are maneuvering at high speed, making it impossible to optimize the coverage of SNR in advance when using terrestrial base stations. The authors of this work emphasized the fairness of service of users since the numbers and coverage of the UAV base station are quite limited in practical applications. By constructing it as described, the user fairness using received signal strength, a multi-agent-based control scheme was implemented for distributed path optimization. Moreover [33], proposed a flow level model for a UAV base station network and a DRL-optimizing method for network traffic balancing based on a realistic UAV radio model. The contribution of the study is the first introduction of a traffic flow model to describe UAV base station communication performance, which was later used as input for DRL-based trajectory control. Furthermore [34], demonstrated a circle trajectory setup for multiple-ground-user coverage.