# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues
### Practical Implementation  
The AI/ML techniques rely, in principle, on massive and high-quality labeled data sets to achieve the desired results. Although the data availability is currently possible and economically and technologically less expensive due to the development of IoT sensor systems and cloud computing, the data collected by sensors and network equipment is usually subject to losses, redundancy, mislabeling, and class imbalance. Therefore, the effectiveness of the training procedure is questionable. Currently, the TensorFlow Lite, an open-source, framework developed by Google for ML inference on low-power embedded devices, can be used in order to succesfully exploit ML and to facilitate object recognition on unclassified data [131]. Moreover, Keras [132], a high-level neural networks \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Placement and Trajectory Target** & **ML Solution** \\ \hline [PERSON] et al. [109] & Throughput & Q-learning \\ [PERSON] et al. [110] & Throughput & GP and NMPC based \\ [PERSON] et al. [111] & Sum-rate & Q-learning \\ [PERSON] et al. [112] & Communication quality & NN \\ [PERSON] et al. [113] & MOS and QoE & Q-learning \\ [PERSON] et al. [115] & Mobility prediction and object profiling & Unsupervised learning \\ [PERSON] et al. [116] & Throughput and path planning & Map compression based \\ [PERSON] et al. [118] & QoE & Q-learning \\ [PERSON] et al. [119] & Sum-rate & Distributed learning \\ [PERSON] et al. [120] & Throughput & ANN \\ [PERSON] et al. [121] & Radio coverage & Q-learning \\ [PERSON] et al. [122] & QoS & Q-learning \\ [PERSON] et al. [101] & Latency & Kernel density estimation \\ [PERSON] et al. [123] & Spectral efficiency & DQN \\ [PERSON] et al. [124] & Coordination of multiple UAVs & Q-learning \\ [PERSON] et al. [126] & Radio coverage & Decentralized DRL \\ [PERSON] et al. [128] & QoS & Double Q-learning \\ [PERSON] et al. [129] & Radio Coverage & DRL \\ [PERSON] et al. [130] & Energy efficiency & SMGD \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Placement and trajectory issues in ML-enhanced UAV networks. application programming interface (API) written in Python, is capable of running on top of TensorFlow and enables fast experimentation. Moreover, today's powerful multi-core central processing unit (CPU) architectures, GPUs, and broad availability of libraries for DL (e.g., [133]) allow for fast, parallel data processing in real-time. However, the limited battery capacity and on-board processing capabilities and power resources of the UAVs severely restrict the application of DL-based techniques, which allow for object detection, depth prediction, target tracking and localization, and decision-making on the fly. In particular, practical constraints mainly regarding the computational power, parallel data processing in real-time, and power consumption restrict the design and implementation of efficient DL solutions on UAVs. These issues are expected to be solved in the near future, as powerful miniaturized computing devices with low-power consumption are an active working field for embedded hardware developers. Hence, future research efforts should be devoted to further investigating and verifying the performance of AI/ML-aided aerial networks, especially in terms of the computation efficiency and hardware design. In this direction, future developments may also include the improvement of the processing time of the learning algorithms by adding a confidence score to predictions and a scale factor to the generated actions, which will lead to the reduction of the number of the required actions. Also, one could combine different ML techniques in order to cooperatively carry out the prediction procedure and thus to improve computational efficiency. Apart from including a larger number of samples for optimization, it could be also interesting to derive the optimal parameters of the learning algorithms to achieve faster convergence.