# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### Previous Survey Works  
Along with the growing number of novel solutions for wireless networks during the last years, several recent surveys focusing on the interplay of AI/ML and wireless communications have been provided. Various studies have proposed the application of ML for improving the performance of wireless networks. The authors in Reference [22] provide a brief description on the use of DL in an architecture based on the aerostack framework, which is an aerial robotics architecture consistent with the common components related to perception, guidance, navigation, and control of unmanned rotorcraft systems, with different robotic agents consisting of different abstraction levels of unmanned aerial robotic systems, like social, reflective, and deliberative layer, among others. The DL algorithms are employed in three directions: feature extraction, planning and situational awareness, and motion control. The classification is limited to the learning type of the algorithm and the field of application for aerial robot systems which use specific sensors. Next, the survey in Reference [23] studied the use of deep reinforcement learning (DRL) for several network and communication aspects, such as network access and rate control, cashing and offloading, security and connectivity preservation, routing, resources, and data collection. In some cases, the UAV is considered as an agent for the cellular network, like a base station, a sensor, a mobile, a user, a network controller, or a scheduler. Deep Q-network (DQN), deep Q-learning (DQL), and liquid state machine (LSM) are some of the learning algorithms used for the aforementioned issues for the UAV scenarios. A rather comprehensive survey from the exclusive angle of DL applications in mobile and wireless network is depicted in Reference [24]. The authors provide classifications for different attributes, such as mobile big data, data and mobility analysis, user localization, network control, and security. However, the use of UAV networks is not taken into consideration. At the same time, several of the DL techniques that are discussed can be of importance to UAV networks. Then, the survey in Reference [25] provides a brief overview of ML techniques for wireless big data analytics. One of the described applications is related to UAVs, still without discussing AI/ML aspects. Another work discusses, among others, that the AI/ML algorithms on edge devices in 5G and beyond 5G networks are necessary for the development of 6G networks [26]. The critical applications of self-sustaining networks (SSNs) in 6G requires low-latency, high-reliability, and scalable AI, along with a reliable infrastructure, relying on the integration of UAVs and ground network nodes. In networks consisting of ground-based and aerial vehicles, there have been various surveys presenting possible applications of AI/ML. First, the exploitation of AI for vehicle-to-everything (V2X) application is given in Reference [27], including comparisons of the AI algorithms. Moreover, in autonomous driving, the survey categorizes the most widely used AI techniques in swarm intelligence, ML, DL, expert systems and planning, scheduling, and optimization, where one may find similarities to the prominent UAV classification. Some of the suggested open source or proprietary software tools can also be used in UAV communications, but most of them consider applications like safety, network congestion, navigation, security, content delivery, and edge computing. Furthermore, the paper in Reference [28] gives an overview of 5G communication aspects for V2X, UAVs, and healthcare use-cases. More specifically, UAV-based wireless networks aspects are mostly discussed for disaster management purposes without considering ML enhancements, while ML is mainly considered for healthcare applications in the area of anomaly detection for patients. Next, the authors in Reference [4] present the integration of UAV networks for 5G and beyond communications.