# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 1 Introduction
Furthermore, in [8], a DNN-based channel estimation scheme is proposed to jointly design the pilot signals and channel estimator for wideband massive multiple-input multiple-output (MIMO) systems. To address the limitations of existing systems, researchers have focused on ML and DL methods in recent years. Despite their enormous potential, DL approaches confront significant difficulties that limit their applicability in advanced communication environments. To develop an adequate mapping from features to desired outcomes, deep NNs (DNNs) require enormous datasets, which raises both the computational complexity and the difficulty of the training process. Due to the current state of communication datasets, it is questionable to rely purely on data-based DNNs as black boxes and leave all predictions to model weights. Model-driven DNNs have evolved as a solution to the limitations of model-based and data-based techniques; these DNNs combine the strengths of DNNs in learning and mapping with domain expertise to maximize benefits [9]. Most model-driven DNN solutions fall into one of two categories: deep unfolded networks, in which DNN layers reproduce rounds of an existing iterative process, or hybrid networks, in which DNNs assist conventional models and boost efficiency. The data scarcity problem has also been eliminated alongside the advancement of DL literature on communication technologies, making room for data-based DL systems [10]. Nevertheless, the promising enhancements of DL techniques for channel modeling have motivated researchers to investigate utilizing different learning methods and feature extraction approaches in the context of cellular-connected UAV communication systems. To the best of our knowledge, only a few prior relevant research works dealt with UAV channel characterizations using the received signal strength (RSS) in cellular communications [11; 12; 13; 14]. In [11], a modeling framework for wave propagation in mobile communications is proposed by combining several learners in an ensemble learning method for RSS modeling. Furthermore, a deep reinforcement learning (DRL) -based model for channel and power assignment is developed in [10] for UAV-enabled IoT systems, where a single UAV-base station is deployed to collect data from multiple IoT nodes. In [13], a learning algorithm is used to predict channel characteristics between UAVs and ground users, which provides accurate environmental status information for UAV deployment decisions. Additionally, the authors in [14] have proposed ensemble methods based on supervised base learning to predict the channel model of UAV using the least squares boosting method, bagging prediction, and support vector machines (SVMs). In terms of DL methods, [PERSON] et al. [15] have proposed a DL scheme that can fully explore the features of wireless channel data and obtain the optimal weights as fingerprints while incorporating a greedy learning algorithm to reduce computational complexity. Beyond this, the restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over a set of inputs in an unsupervised manner. RBM is bipartite, i.e., there are no intra-layer connections, and it consists of a pair of layers that are commonly referred to as the _visible_ and _hidden_ units, respectively, as shown in Figure 1, and they may have a symmetric connection between them. However, RBM has some limitations due to dealing with only binary data, and to circumvent this issue, the Gaussian-Bernoulli restricted Boltzmann machine (GRRBM) [16] was proposed to process real data where a Gaussian visible node replaced a binary node to initialize DNN for feature extraction and dimension reduction. The commonality of the works as mentioned earlier [11; 12; 13; 14; 15] is that they all follow training methods that have high variance and slow diverging behaviors that require a massive volume of training data and extended training time, which is prohibitive in wireless communication systems.