# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning  
In the novel 3-D and dynamic architecture that is introduced in the UAV-cellular networks, resource management, network planning, content-caching, and user association tasks are very demanding, since many contradictory requirements should be considered, such as low latency, increased throughput, low overhead, support of massive number of devices, and dynamic conditions. In that sense, the ML framework has been adopted to facilitate resource management in a quite efficient manner. In Reference [61], the authors aimed to predict the success and failure rates in an UAV network by using ML algorithms based on linear regression (LR) and SVM. Since UAV connectivity is time \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Security Target** & **ML Solution** \\ \hline [PERSON] et al. [51] & Eavesdropping mitigation & Q-learning \\ \hline [PERSON] et al. [52] & Interference and jamming mitigation & PHC-based learning \\ \hline [PERSON] et al. [53] & Jamming, spoofing, and eavesdropping mitigation & RL \\ \hline [PERSON] et al. [54] & GPS spoofing protection & ANN-supervised learning \\ \hline [PERSON] et al. [55] & Eavesdropping detection & One-class SVM and K-means \\ \hline [PERSON] et al. [56] & Interception of malicious UAVs & Q-learning \\ \hline [PERSON] [35] & Real-time mapping & Genetic algorithm-based \\ \hline [PERSON] et al. [57] & UAV pilot identification & Classification based on LD, QD, SVM, KNN, or RandF \\ \hline [PERSON] et al. [58] & Guaranteeing privacy, integrity, and confidentiality \\  & of UAV compressed video streams & CNN-based detection \\ \hline [PERSON] et al. [59] & Protection against trespassing UAVs & SVM-based ML \\ \hline [PERSON] et al. [60] & Small boat detection & DL-based CNN \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Security issues in ML-enhanced UAV networks. variant due to their mobility, the success probability of the transmission decreases as the wireless links' distance increases. In this setting, ML methods can train the UAVs to determine whether connectivity with neighboring nodes can be achieved. Simulations revealed that accurate training by LR and SVM can be attained with SVM offering improved precision and speed. Through timely training, multi-UAV networks can be optimally deployed and accurate clustering can increase the reliability of wireless transmissions. In Reference [62], ML techniques have been applied to predict cell quality for UAVs that are connected to a cellular network. More specifically, a new conditional random field position is proposed for predicting the best serving cell for one node at a given location. The main idea is to exploit the spatial correlation that naturally exists in aerial channels nearby positions. For evaluating the performance of the proposed approach, real 3 GPP LTE simulation parameters have been assumed for the cellular model. The numerical results have shown that the proposed scheme offers high accuracy and improved performance as compared to other heuristics methods. Then, in Reference [63], UAVs and device-to-device (D2D) communications are jointly employed to recover the connectivity in a disaster area. To this aim, two UAVs have been used for serving as aerial BSs. The goal is to select the users to be served by the two UAVs (or via D2D connections) that maximize the weighted sum-rate. This user association problem can be seen as a clustering one, and thus, a k-means based algorithm has been proposed in order to solve this type of unsupervised learning problem. In this context, two algorithms are given, i.e., a learning-based clustering algorithm and a relaxed optimization algorithm. The numerical results have shown that the learning-based algorithm achieves a relatively high performance close to the benchmark relaxed optimization algorithm at a much lower computational complexity. In Reference [64], an RL algorithm is proposed for reducing unnecessary handoffs in UAV-based networks. In addition, a k-means method is also adopted as a mobility control approach.