# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Placement and Trajectory Design  
This challenge is also directly related with the nontechnical but equally important legal and regulatory issues of constructing an international legislation framework for UAV operation [105]. These regulations do not permit UAVs to fly over all areas and introduce altitude limitations as well. Note that there exist some common rules across different countries: (i) the UAVs should fly only in visual line-of-sight (VLoS), usually limited to 500 m from the pilot; (ii) the UAVs should fly only under 120 m relative to the ground. For other operations, such as flying over people, cities, beyond VLoS \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Detection and Localization Target** & **ML Solution** \\ \hline [PERSON] et al. [88] & Identification of UAV type over encrypted & \multirow{2}{*}{MLE and l1-norm regularization} \\  & Wi-Fi traffic & & \\ \hline [PERSON] et al. [89] & AU detection from measured RF data & Bayesian, SVM, and MLP \\ \hline [PERSON] et al. [92] & UAV classification from measured RF data & ANN \\ \hline [PERSON] et al. [93] & UAV classification from raw RF signal & \multirow{2}{*}{
\begin{tabular}{l} Markov-based naive Bayes \\ detection, KNN, DA, SVM and \\ NN-based classification \\ \end{tabular} } \\  & fingerprints & & NN-based classification \\ \hline [PERSON] et al. [94] & Detection of UAV flying path & SVM and CNN \\ \hline [PERSON] et al. [96] & Detection of UAV flying status & J48, RandF, and NN \\ \hline [PERSON] et al. [100] & UAV controller detection from transmitted & \multirow{2}{*}{CNN} \\  & control signals & \\ \hline [PERSON] et al. [101] & Detection of UAV-UEs from sparse & \multirow{2}{*}{Kernel density estimation} \\  & information & \\ \hline [PERSON] et al. [102] & Detection of UAV type and flight mode & \\  & from raw RF signals & DNN \\ \hline [PERSON] et al. [104] & UAV detection in a swarm & DL \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection and localization issues in ML-enhanced UAV networks. (BVLoS), etc., special permissions can be granted depending on the country, while testing of new systems and applications must be usually done in segregated airspace areas. Moreover, flying over people not involved in the operation is not allowed in most countries or it is allowed only under a specific authorization of the aviation authorities. Several studies on the optimized placement and trajectory of UAVs as BSs or relays have been previously conducted (e.g., References [106; 107]). These studies have focused on providing efficient connectivity to a group of distributed ground users. The joint optimization of trajectory and power control in multiple UAVs scenarios was obtained in Reference [108] aiming at maximizing the users' throughput and satisfying the users' rate requirement. The optimization problem relied on ML techniques and included three steps in order to determine the trajectory design of UAVs acting as agents: (i) a multi-agent Q-learning-based placement algorithm was proposed for estimating the 3-D optimal placement of the UAVs with respect of the initial positioning of the ground users, and (ii) the mobility and the upcoming positioning of the ground users were predicted using an ESN-based prediction algorithm [109] and a real-world geographical data set. The latter one was collected from an online social network, i.e., Twitter, as input and included GPS coordinates of anonymous users and recorded time stamps. The third step of the optimization problem included a multi-agent Q-learning-based algorithm that was used to determine the optimal position and to transmit power of the UAVs in each time slot according to the users' mobility. The results underlined that the proposed algorithm can converge to an optimal state while comparisons with two benchmark schemes, i.e., the historical average (HA) model and the LSTM depicted the superiority of ESN in terms of MSE over both HA and LSTM models at a lower complexity.