# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### Previous Survey Works
Several newly founded techniques are considered (e.g., non-orthogonal multiple access (NOMA)and millimeter wave band (mmWave) transmissions), while a classification of different techniques is performed in terms of physical layer; network layer; and joint communication, computing, and caching, taking into consideration the BS type and the number of UAVs. However, only a limited number of works on the application of ML in UAV networks is included. Then, the survey in Reference [6] offers a thorough overview on using UAVs for cellular communications in view of experimental and simulation channel studies that have been performed in such networks, along with prototyping. In the latter, only two platforms are presented making use of ML algorithms for trajectory and placement. The rest of the paper is dedicated to standardization, regulation, and security aspects, while ML is only included as a possible future research direction for UAV networks. From the cyber-security point of view, one may find a limited number of survey papers, studying AI/ML. The article in Reference [29] provides a well-structured review of UAV networks from a cyber-physical system (CPS) perspective. The three CPS components in the UAV networks that the survey analyzes are communication, computation, and control. Regarding AI/ML techniques, the authors classify open-source software projects and libraries, mainly focusing on the machine vision area. The authors briefly mention the use of ML and RL at the physical layer and routing, correspondingly, at the computation and communication components. Regarding the computation and control components, intelligent algorithms are discussed for computation-enhanced flight and formation control. The survey in Reference [30] does not focus on communication issues, while the authors provide a mapping of UAV network connectivity, QoS, security, design challenges, and requirements for cyber-physical (CP) security applications. The application of ML is presented for object detection and image recognition, as well as for interesting future research areas, such as collision avoidance, spectrum sensing, channel estimation, and energy management. The main goal of Reference [31] is to outline the wireless and security challenges that arise in the context of UAV-based delivery systems, real-time multimedia streaming, and intelligent transportation systems. To address such challenges, artificial neural network (ANN)-based solution schemes are introduced. The authors consider the security at higher communication layers. Regarding the wireless challenges and relevant AI/ML solutions, each use case follows a different approach and ANN-based solution. Also, the authors provide a discussion on physical layer communication issues, such as interference management. Finally, the work in Reference [5] provides a comprehensive study on the use of UAVs in wireless communications and networks from the CP security perspective. Two main use cases of UAVs are investigated: aerial wireless BSs for 5G and beyond applications that complement emerging wireless communication systems and cellular-connected users that use existing wireless infrastructure. For each use case, key challenges, applications, and fundamental open problems are noted, along with the mathematical tools and techniques needed for addressing the challenges. Regarding the latter, the tutorial provides only a small section discussing the emerging field of ML, primarily for trajectory and path planning. The majority of surveys either focus on ML for different wireless network environments or foresee the integration in UAV-based networks, discussing its future potential. Nonetheless, three recent works can been identified as more similar to this study. First, the survey in Reference [32] focuses on AI for robotic applications, comprising both ground-based nodes and UAVs. In such scenarios, UAVs can improve the connectivity and security of multi-robot teams towards efficiently performing their tasks.