# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 1 Introduction  
Low-altitude unmanned aerial vehicles (UAVs), also called drones, have enabled many personal and commercial applications, including aerial photography and sightseeing, parcel delivery, emergency rescue in natural disasters, monitoring and surveillance, and precision farming [1]. Recently, the interest in this emerging technology is steadily surging as many governments have already facilitated the regulations for UAV usage. As a result, UAV technologies are being developed and deployed at a very rapid pace around the world to offer fruitful business opportunities and new vertical markets [2]. In particular, UAVs can be employed as aerial communication platforms to enhance wireless connectivity for ground users and Internet of Things (IoT) devices in harsh environments when terrestrial networks are unreachable. Additionally, intelligent UAV platforms can provide important and diverse contributions to the evolution of smart cities by offering cost-efficient services ranging from environmental monitoring to traffic management. Wireless communication is a key enabling technology for UAVs, and their integration has drawn substantial attention in recent years. In this direction, the third generation partnership project (3 GPP) has been active in identifying the requirements, technologies, and protocols for aerial communications to enable networked UAVs in current long-term evolution (LTE) and 5G/B5G networks [3]. The communications of UAVs fundamentally differ from terrestrial communications in the underlying air-to-ground propagation channeland the inherent size, weight, and power constraints. The 3D mobile UAVs enjoy a higher probability of line-of-sight (LoS) communication than ground users, which can be beneficial for the reliability and power efficiency of UAV communications. Nevertheless, this also implies that UAV communications may easily cause/suffer interference to/from terrestrial networks, which has to be carefully addressed [4]. The availability of LoS depends not only on the propagation environment but also on the UAVs' altitude, elevation angle, and movement trajectories, which have to be jointly evaluated for each scenario. Realizing full-fledged UAVs in the 3D mobile cellular network depends largely on the reliability and efficiency of the communication channels over diverse UAV operating environments and scenarios. Furthermore, these channels are crucial for designing and evaluating UAV communication links for control/non-payload and payload data transmissions across novel UAV operation scenarios, which is one of the significant challenges in this setting. Moreover, the mobility of UAVs and the time-varying topology of the 3D mobile network, along with localization errors and latency, may complicate acquiring a timely and accurate knowledge of channel state information (CSI). Therefore, obtaining accurate channel modeling is paramount for designing robust and efficient beam-forming and beam-tracking algorithms, resource allocation methods, link adaptation approaches, and multiple antenna techniques. While several statistical air-to-ground channel models that trade off between accuracy and mathematical tractability have been proposed in the literature [5], more practical analysis to bridge this knowledge gap is still needed. On a parallel avenue, the wireless communication community has paid significant attention to deep learning (DL) techniques owing to their success in various applications, e.g., computer vision, natural language processing, and automatic speech recognition. DL is a neuron-based machine learning approach that can construct deep neural networks (DNNs) with versatile structures based on the application requirements [6]. Specifically, several works in the open literature have utilized DL methods for channel modeling and CSI acquisition. For instance, a DL-driven channel modeling algorithm is developed in [7] using a dedicated neural network based on generative adversarial networks designed to learn the channel transition probabilities from receiver observations.