# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### UAV Characteristics  
As the demand for comprehensive broadband services, global coverage, and ubiquitous access has grown, non-terrestrial networks (NTNs) can strongly support the well-established terrestrial backhaul networks [2]. Among the principle components of NTNs, the low-altitude platforms (LAPs) intend to facilitate various civilian, commercial and governmental missions, as well as IoT applications [3], ranging from military and security operations to entertainment and telecommunications. UAVs, the major representative type of LAPs, is usually small unmanned aircrafts employed for short time periods (up to several hours) allowing the rapid deployment of a multi-hop communication backbone in challenging applications without any personnel involved, such as public safety, search and rescue missions, surveillance inspection, emergency communications in post-disaster situations or unexpected events, photographic reconnaissance, urban traffic surveillance, precision agriculture, and media traffic monitoring [4; 5; 6]. It is worth noting that the market research forecasts that the sales of UAVs will exceed $12 billion per year by 2021 [7], whereas the Federal Aviation Administration (FAA) predicts that the UAVs will count about 2.4 million units by 2022 [8]. It is obvious that such market size and dynamics have been the driving force towards the evolution of UAVs. Recently, great interest has arisen by standardization bodies, industries, and academia towards the use of UAVs as flying BSs, mobile relays, or autonomous communicating nodes for providing low latency and highly reliable communications in cities, across suburban areas, and over rural terrains. The use of aerial vehicles for Long-Term Evolution (LTE) was suggested by the 3 rd Generation Partnership Project (3 GPP) [1], whereas the notion of nomadic relay was presented by the IEEE 802.16s Relay Task Group [9]. In 2013, realistic UAV frameworks have been also established by the special committee (SC-228) formed by the Radio Technical Commission for Aeronautics (RTCA) in an effort to frame performance specifications for the operation of UAVs [10]. Moreover, in 2016, RTCA constituted a drone advisory committee to safely introduce the UAVs into the national airspace system [11]. The National Aeronautics and Space Administration (NASA) and FAA have also launched a joint research initiative to integrate UAVs in national airspace system across the United States [12]. From an industry perspective, major vendors, i.e., Google, Facebook, Microsoft, Qualcomm, Nokia, and Huawei, have also tested the operability of aerial platforms for current LTE and future fifth-generation (5G) applications [13; 14; 15; 16; 17; 18]. Depending on their flying mechanisms, UAVs can be classified into remotely piloted vehicles (RPVs), multi-rotor drones (also known as rotary-wings drones), fixed-wing drones, hybrid fixed/rotary wing drones, robot planes, and pilotless aircrafts and can differ in size from small toys to large military aircrafts [4; 5; 6]. Also, the payloads of UAVs including communication equipment, cameras, radars, and sensors vary from tens of grams up to hundreds of kilograms and directly determine their size, the battery capacity, and the flight duration. Owing to their unique characteristics, the UAVs are capable of providing ubiquitous and cost-effective wireless access over large coverage areas at high elevation angles and modest altitudes and with high chance of line-of-sight (LoS) connection with the ground nodes, while they ensure rapid deployment and movement on demand [4; 5; 6].