# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Path Planning for Aerial Mobile Relay  
Recently, UAVs have been considered popular platforms for communication relay purposes due to their deployment flexibility and maneuverability. In contrast to satellite communications, UAV relay systems hold the potential advantages of high-volume and low-latency communication through line-of-sight links. A UAV-aided mobile relay is utilized to transmit information from ground users to terrestrial or non-terrestrial base stations, which is of great importance in scenarios such as disaster management for extending communication ranges over mountainous terrain. The placement and trajectory of UAV mobile relays have a significant influence on communication distance and the quality of service for wireless users. Typical application scenarios involving UAV relays include movable robotic platforms, wireless sensor nodes and internet of things devices. In such cases, the transiting power of the ground nodes is relatively low compared to UAV relays, indicating that deployment location and flight trajectory have a major impact on the relay link, thus requiring the careful optimization of the motion parameters. From the literature [37; 38; 39; 40; 41; 42; 43], investigated trajectory optimization methods concerning the condition of the ground user. Furthermore [37], proposed an iterative solution for joint optimization of UAV trajectory, power consumption, and time-slot assignment, which is modeled as a non-convex problem. The joint optimization of trajectory and power is the foundation of communication resource allocation. Therefore [38], considered a path planning and power consumption optimization paradigm for a space-air-ground integrated relay network in a non-orthogonal multiple access scheme. The results of the proposed paradigm outperformed that of a fixed circle trajectory. Moreover [39], investigated a novel solution for trajectory design and transmitting power minimization for a UAV-assisted relay system. The study considered a cognitive relay scenario in which the trajectory was designed according to the speed and altitude constraints of the relay system. In [40], an efficient algorithm was designed for a UAV relay's three-dimensional trajectory optimization in order to achieve a higher-throughput network in the case of the interference of ground users of the same frequency band. The trajectory optimization was conducted considering the influence of intentional jamming. Similarly [41], studied a multi-UAV trajectory optimization framework in which UAVs acted as both aerial base stations and wireless relays. The study is of great significance for large-scale communication coverage scenarios in which UAV base stations and UAV relays collaborate to complete the network coverage task. 3. **UAV Path Planning for Cellular-Connected UAV User Equipment** Except for the fact that UAVs have been successfully utilized as flying base stations and relay nodes, recently, UAVs performing practical tasks tend to be connected to ground cellular base stations as user equipment, which brings about the issue of optimal path finding considering connectivity constraints and ground interference [42; 43]. A study [44] proposed an analytical model to demonstrate the influence of trajectory and altitude on the quality of service in relation to UAV cellular users. From the study, we can see that with the UAV moving from the center to the edge area of the base station coverage, the minimum constrained altitude decreases. The study indicated that the motion parameters should be well considered for better UAV user equipment quality of service. Moreover [45], investigated an iterative method for trajectory planning to minimize the time required to fulfill the task and overall power consumption for UAV aerial users. The proposed method considered a minimum task fulfillment time under the signal-to-noise ratio requirements. Furthermore [46], proposed an online trajectory-optimizing scheme for minimal power consumption based on an outage probability map reconstructed from sparse sampling.