# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Placement and Trajectory Design
Since initially, the UAVs have imperfect CSI; the optimization problem was formulated by exploiting game theory. Then, a robust and distributed learning algorithm was proposed to guide multiple UAV flight plans in order to maximize the sum-rate with fairness for all ground users under specific flight region and power constraints. This algorithm converged to a stable state of maximizing the aforementioned optimization objective, resulting in a guide that facilitated the flight path of the UAVs. A flight path planning model for a dynamic and auto-configurable FANET was presented in Reference [120]. This model took advantage of a metaheuristic optimization-based approach that intends to optimize in real time the position of a flying relay device, i.e., an UAV, in order to obtain the best throughput while having knowledge of the position of the other UAVs. Thus, an ANN was trained using mobility data of UAVs including the position and traffic information that was supplied through the Network Simulator 2 (NS-2). According to the results, the average throughput of the optimized FANET was significantly higher, i.e., 135%, in comparison with the non-optimized FANET in an area of 200 \(\times\) 200 m.  
In Reference [121], a Q-learning positioning approach was proposed in an effort to find the best 3-D placement of drones in multiple drone small cells considering propagation areas, where the conventional terrestrial communication infrastructure is not operational or accessible due to a large-scale natural disaster. In these temporary intelligent small cells, the drones have limited resources and the ground users could have distinct requirements in terms of the data throughput and the mobility characteristics. The goal of this approach was to facilitate the construction of an efficient emergency communication network by maximizing the total network radio coverage while attaining robustness against dynamic network conditions, mobility issues, and interference. The simulation results included performance comparison between the proposed Q-learning method and random fixed positioning and circular positioning strategies and depicted that the former outperforms the others, in terms of coverage, QoS, and backhaul throughput. The performance of a downlink air-to-ground communication system was optimized in Reference [122], where an aerial platform acted as a BS and the ground users were not static. In particular, a low-complexity Q-learning algorithm was used to find, in relatively short processing time compared to heuristic algorithms, the 3-D optimal position of the aerial BS in such a dynamic environment, where the network topology continuously changes, satisfying the QoS requirements. Thus, the benefits of RL-based positioning is evident, as the need for reinitialization in the heuristic-based approaches is avoided and gradual changes due to UEs mobility are efficiently monitored, leading to positioning with lower complexity. In Reference [101], a 3-D cellular network was also proposed that utilizes drones as BSs and drones as UEs. The former use ML tools to determine the spatial probability distribution of the latter for a certain time period, considering the mobility properties and aiming at minimizing the latency. In particular, a kernel density estimation method was developed, for which the training is attained using the sparse (owing to the excessive overhead costs) available prior information of the location of drone-UEs. Additionally, the problem of the proper placement of an aerial platform as a BS was investigated in Reference [123], taking into account the users' requirements and specific scenarios. Specifically, a DQN algorithm was proposed that maximizes the spectral efficiency by exploiting a large-scale pre-learning experience of different user layouts. The DQN combines the DL CNN and reinforcement learning Q-learning advances, but it has the advantage of being more time efficient.