# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues
### Channel Modeling  
The accurate prediction of channel behaviour in wireless networks is of utmost importance, especially in highly mobile environments. The selection of appropriate channel models is necessary for performance evaluation, network planning, and UAV trajectory design among others. Towards this end, various ML solutions have been proposed for predicting the channel behaviour and UAV received signal strength (RSS). ML techniques are capable of assessing the wireless conditions without requiring a vast amount of data sets, as it is the case in channel modeling from extensive measurement campaigns. Motivated by the scarcity of UAV measurements in real environments, the authors of References [36; 37] use ANNs and ensemble methods for predicting the RSS at the UAVs. First, in Reference [36], a multiple-layer perceptron (MLP) NN is used and trained by measurement data. Novel training methods are developed based on the combination of self-adaptive differential evolution (DE) algorithms with the Levenberg-Marquardt (LM) method. The sequential application of DE and then LM method offers better initial starting weights compared to the case of random weight generation, resulting in better solutions and faster convergence. Therefore, various new algorithms for ANN training can be devised employing DE in the exploration phase and LM in the exploitation phase. The authors propose to use the success-history-based adaptive DE with linear population size reduction (L-SHADE) algorithm for ANN training. L-SHADE self-adapts the control parameters and performs linear population size reduction. Performance comparisons with four DE algorithms showed  
Figure 2: Classification of the AI/ML solutions in UAV-based communications. that L-SHADE provides the best performance due to its unique features, being a promising solution to extract channel model characteristics without relying on large amounts of measurement data. The study in Reference [37] focused on ensemble methods for UAV RSS prediction. The operation of ensemble methods was based on training combinations of supervised learning base learners, i.e., methods such as decision trees or NNs, among others. This work extended the NN-based algorithm of Reference [36], developing a new ensemble method relying on five base learners, namely support vector machines (SVMs), GP, ANNs, least squares boosting method (LSBoost), and bagging prediction. At the same time, the ML-based solution adopts the salp swarm algorithm (SSA) evolutionary algorithm, inspired by the swarm movement and foraging of salps in the oceans. In greater detail, the behavior of salps is modeled by dividing them in two groups, consisting of leaders and followers. The first group includes salps with higher objective function values, while the second group contains salps with lower objective function values. The proposed models are validated by appropriate performance indicators, i.e., the mean absolute error (MAE), the root mean squared error (RMSE), and the mean absolute percent error (MAPE). It was shown that, by using the ensemble method, the accuracy of channel modeling can be improved when compared to NN-based approaches, proving that ensemble learning can be a valuable tool to solve problems in the field of electromagnetics. In another line of research, the study in Reference [38] examines real-time three-dimensional (3-D) wireless channel modeling of the air-to-ground channel among UAVs and ground nodes, using unsupervised learning. In greater detail, a clustering technique is employed to analyze the 3-D wireless channel LoS and Non-Line-of-Sight (NLoS) channel states based on RSS measurements by the UAV. Additionally, PL and shadowing parameters are derived, formulating an appropriate temporary 3-D channel model. As an online learning approach is followed, changes in the communication environment are integrated in the temporary 3-D channel, increasing its accuracy.