# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues
### Channel Modeling
As shown in the performance evaluation, employing an online learning method allows temporary 3-D channel modeling to be derived according to dynamic changes of the wireless environment. As a result, channel modeling accuracy can be significantly increased in UAV communication scenarios. Nonetheless, the impact of shadowing is not considered in this work, limiting the accuracy of the 3-D channel modeling. In Reference [39], a method that optimizes the positioning of UAVs acting as aerial relays in air-to-ground wireless networks was presented. This method enables the autonomous path planning of UAVs by exploiting a finely structured radio map. Since this map cannot be easily obtained in practice due to the complex terrain in common urban propagation areas, a joint clustering and regression problem using a maximum likelihood approach was formulated based on the K-segment ray tracing model. In particular, an ML-based iterative algorithm was employed, acting as a channel predictor in an effort to reconstruct the radio map from a limited number of training samples via a kernel-based method. The results pointed out that the proposed method requires almost as few as one-tenth of training samples to achieve the reconstruction performance of the K nearest neighbor (KNN) baseline method, while it can recognize the propagation segments and, hence, optimizes the placement of the UAVs. Furthermore, the accurate modeling of PL prior to the installation and operation of a wireless network is a complex process requiring data from a detailed link budget analysis. Currently, radio engineers mainly adopt deterministic and stochastic methods for estimating signal attenuation due to PL. It must be noted that, in irregular terrains characterized by vegetation, hills, and buildings, PL models provide limited accuracy of signal attenuation. In order to separate the impact of these factors on the PL, the work in Reference [40] relied on geo-referenced satellite imaging and 3-D point cloud in order to extract the environmental characteristics through 3-D image classification and to feed them in ML-based PL modeling for improved wireless network deployments. Therefore, light detection and ranging (LiDAR) and ANNs are employed, resulting in a simulation environment identifying features from the environment, such as tree foliage. Then, these features are used to adjust the RSS calculation through the PL model. Performance evaluation was based on MAPE, revealing that the proposed PLprediction method significantly outperformed existing empirical PL models, such as COST 231-Hata and log-normal, due to the consideration of environmental irregularities. In greater detail, empirical PL models exhibited RSS estimation errors within 6.29-16.9% from the actual RSS measurements while the proposed ML-based solution significantly reduced the estimation error to 4.26%. Still, the effect of buildings was not considered, limiting the applicability of this method in urban settings. Aiming to study the application of ML on PL prediction for the air-to-air channels, the authors of Reference [41] proposed various ML-based PL models for urban environments. The considered ML algorithms consisted of the random forest (RandF) and KNN. RandF is an ensemble method stemming from decision trees. In order to perform classification, it collects classifications from all the decision trees and selects the most popular prediction as its output, forming a strong classifier from a large number of weak or weakly correlated classifiers. KNN performs supervised learning based on a similarity measure, calculating the distance between points on a graph in order to solve classification or regression problems. Then, using ray-tracing to generate training and testing data, the prediction accuracy of the ML-based PL models was compared with the empirical Stanford University Interim (SUI) and the COST231-Walfisch-Ikegami models.