# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Placement and Trajectory Design
These results also illustrated that the proposed approach can increase the throughput by approximately 17%, while accuracy improves, as the size of the reservoir increases. The authors of Reference [110] follow a Gaussian Process (GP) approach to derive the air-to-ground communication channel, providing an additional link to a group of ground nodes, thus improving their communication quality. For this purpose, GP is employed for predicting the communication channel strength at random UAV positions in an urban environment. Then, the channel model can be used to perform optimal UAV trajectory planning, either with offline pre-scanning based on GP, followed by a nonlinear model predictive control (NMPC) planner or NMPC planner with online GP. More specifically, in the offline method, the UAV performs data measurements from ground nodes via a prespecified scanning pattern flight. After, these data are fed to the GP to build the channel strength between the different UAV positions and ground nodes. On the contrary, in the online method, the UAV trajectory is designed according to the current knowledge of the air-to-ground channel and through periodic measurements by the UAV incomplete map using GP. It was shown that the offline creation of the communication channel strength map, with GP prior to the start of the mission, outperforms the online creation of the map during the mission without scanning. In Reference [111], the trajectory of an UAV BS employed to provide communication services to multiple users was optimized targeting sum-rate improvement using Q-learning. To model the air-to-ground channel between the UAV BS and the ground users, the log-distance path loss model was used. The simulation scenario included an UAV BS flying at a constant altitude and acting as an autonomous agent, two static ground users, and a cuboid obstacle. To obtain Q-function approximators, a standard table-based approach and an NN were utilized. Based on the simulation results, the UAV BS was capable of learning the network topology without preexisting knowledge of the environment. Also, the UAV BS could autonomously obtain its landing position in a timely manner. These results also underlined that NN is more efficient and scalable and requires significantly less training data than table-based Q-learning. In topologies where mobile ground nodes require UAV relaying, the paper in Reference [112] proposed to combine ML-based measurement with a probabilistic LAP channel model to facilitate UAV trajectory planning. More specifically, the authors assumed four distinct types of urban environments with unknown building position and shape and a wireless channel including the effects of path-loss, multi-path fading, and shadowing with empirically-known distributions. Also, the ground nodes'current positions are available to the UAVs but their mobility patterns are not known. Thus, in order to select the appropriate probabilistic LAP model, UAVs collect a pair of signal strength and elevation angle measurements among the UAV and ground nodes. This data allows a neural network (NN) predictor to determine the current urban environment type. The NN's output can be used for UAV trajectory design using a a Cross Entropy Optimiser (CEO) to generate a set of possible trajectories. Then, a convergence criterion is imposed and, when it is fulfilled, the optimal trajectory is fed to the UAVs. By periodically performing this process, channel prediction for trajectory design in scenarios with mobile ground nodes can be efficiently achieved. Therefore, the NN-based trajectory planning exhibits promising performance in settings where little information on the mission area is available while considering the mobility of the network nodes. The non-convex nondeterministic polynomial-time hardness (NP-hard) problem of jointly handling in real-time the 3-D deployment and the dynamic movement of multiple UAVs was studied in Reference [113], where the goal was to maximize the sum mean opinion score (MOS) of ground mobile users while attaining an adequate QoE.