# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues
### Transmission Parameters Configuration  
In order to maintain reliable communication and efficient utilization of wireless resources, optimal configuration of the transmission parameters should be performed. The study in Reference [46] focuses on adaptive modulation and coding (AMC), proposing a heterogeneous deep model fusion (HDMF) method. The problems of feature extraction and classification are solved through the adoption of DL while obtaining filters based on learning for reducing computational complexity without compromising classification accuracy. Training data are acquired through a real geographical environment forming the basis for the classification tasks. In greater detail, various single-carrier modulation samples with additive white Gaussian noise (AWGN) and multi-path fading are generated for various signal-to-noise ratio (SNR) values. Then, CNNs and long short-term memory (LSTM) are used for time-domain processing of the modulation signal's waveform. CNNs and LSTM are combined accordingly in serial and parallel modes tackling the AMC problem, thus resulting in two HDMFs. Both HDMFs are trained in order to learn the transmission features and to make appropriate classifications to determine the employed AMC. The results show that HDMFs exhibit improved performance compared to single CNN or LSTM for SNR ranging between 0-20 dB.  
In massive multiple-input multiple-output (MIMO) systems, precoding matrices are essential to improve the performance of the transmission by exploiting the channel state information (CSI). As UAVs can be repositioned in order to achieve LoS conditions with ground users, they are considered as an enabling technology for mmWave communications, and the construction of the precoders can be viewed as a training process. In Reference [47], a hybrid precoding architecture based on a lens array for UAVs was proposed, enabling high efficiency at a low cost. Therefore, a robust and energy-efficient hybrid precoding architecture is developed, using the ML cross-entropy (CE) and the relative error estimation optimization methods. In the initial phase, the hybrid scheme randomly generates a number of predicted analog precoders according to the probability distribution, measuring their achievable sum-rates based on the traditional CE algorithm. Then, the predicted precoder is weighted according to the achievable sum-rates. Simultaneously, the relative error between the updated prediction of the precoder and the near optimal probability distribution is calculated, while the relative error is being updated. In the final phase, an analog precoding matrix close to the optimal probability distribution is calculated. Simulation results outline that, even though the proposed algorithm results in reduced sum-rate compared to full digital precoding, due to imperfect system architecture and system array gain losses, it can provide close to optimal performance with low-cost hybrid precoding. At the same time, the hybrid precoding scheme is a feasible alternative for reduced energy consumption compared to full digital precoding, antenna selection, and conventional CE solutions.  
Table 2 includes various communications aspects that have been examined in relevant works and respective ML solutions that were followed to enhance their performance.