# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Path Planning for Aerial Mobile Relay
The contribution of this work was the introduction of an outage probability map through which a trajectory planning method considering power consumption dramatically reduced the outage of connection between the UAV user and the base stations. However, the reconstruction of the outage probability map database cannot be constructed in advance and are energy-consuming, which are major drawbacks of the method. However, in [47], a DRL-based UAV user optimization method was utilized considering the impact factors of UAV power, flight trajectory, antenna pattern, interference from other ground base stations, and other environmental factors. The optimization scheme was converted to a Markov decision process, which can be solved via the deep Q-learning method. Researchers [48] considered a cellular-connected UAV for synthetic aperture radar sensing tasks using the successive convex approximation method. Integrating communication and sensing is a major aspect of future 6G technology, while the proposed trajectory optimization method considered the sensing resolution requirements and converted the non-convex problem to a convex one using the successive convex approximation (SCA) method. Researchers [49] proposed a dynamic programming algorithm to optimize trajectories of cellular-connected UAVs for communication enhancement with considerations in terms of flight altitude. The trajectory planning process considers flight path and altitude at the same time since the altitude of the UAV affects the backhaul performance of the cellular-connected UAV to a great extent. The authors in [50] reported a path-planning method for task-critical missions in which connectivity maintenance is of utmost importance. The signal map of the ground base station was constructed in advance to support the DRL-based algorithm for the optimal UAV path. Similarly [51], reported a UAV curvature design for UAV users with a fixed start and final locations. This work was designated to minimize the flight duration time of the cellular-connected UAV, which has a constrained outage duration upper bound during flight. Researchers [52] emphasized the role of path planning in the effort of interference cancellation. The joint optimization method applying trajectory adjustment and interference cancellation distinguished the UAV users from the ground users to achieve better performance. The existing works on UAV trajectory/path planning in NTN communication is summarized in Table 1. Apart from DRL-based UAV joint trajectory and communication optimization methods, other non-convex optimization paradigms have been utilized to solve the problem. Researchers in [53] investigated a coordinate multi-point transmission method-based mobility control method considering both the displacement and vertical movement of cellular-connected UAVs. In addition, multi-UAV path planning is also considered a major issue in UAV NTN communication. A study in [54] considered a mixed-integer linear program scheme for multi-UAV base station motion optimization. Furthermore, reference [55] proposed an iterative single-head attention approach for path planning and communication optimization for a group of UAVs through distributed control. Moreover, reference [56] introduced a method for maintaining the freshness of information received by base stations while optimizing the UAV trajectory at the same time in a wireless sensor information collection scenario.