###### Abstract
Although attenuation factors, such as path loss and large-scale fading, are lower in LoS communications, in altitudes above the BS antennas' height, the drone communications suffer from both uplink and downlink interferences [15], as in higher altitudes, the drone is in LoS of adjacent/interfering cells as well. Figure 1: Illustration of cellular-connected UAV. On the other hand, cellular networks are optimized for terrestrial UEs, e.g., BS antennas are tilted downward to prevent inter-cell interference and provide service for the UEs via antennas' main lobes [16]. Meanwhile, by increasing the drone flight height, the probability of being served by the antennas' sidelobes will increase, causing severe challenges, such as degradation of the received signal strength and quality [14], and increasing the frequency of HO and outage probability [9]. To further study the performance and quality of the communication link between the terrestrial BSs and UAVs, this article aims to propose machine learning (ML)-based models for predicting reference signal received power (RSRP) and reference signal received quality (RSRQ) in UAV-to-BS communications. It should be noted that, in this study, it was decided to use RSRP and RSRQ instead of path loss to manifest the final output in a more meaningful way. Both RSRP and RSRQ are key parameters directly representing the state of network signal level and quality at the UE location in 4G/LTE and 5G NR networks [17]. Additionally, modeling path loss requires knowledge about eNB parameters, including antenna gain, azimuth, feeder loss, power, etc. The latter are mostly difficult to obtain because of several technical and security concerns. Accordingly, a measurements campaign was conducted in a suburban environment to measure RSRP, RSRQ, and other important network parameters under different distances and flight heights. Then, the collected data were preprocessed and analyzed to investigate the cellular-connected UAV system's performance under different considered scenarios. Finally, ML techniques have been used to develop the prediction models for RSRP and RSRQ. The key contributions of this study are summarized as follows:  
* A comprehensive measurement campaign has been conducted in a 4G LTE network within a suburban environment, consisting of about 28,000 physical layer samples. The performed aerial drive test focused on mobile link reliability between UAV and terrestrial BS. Hence, parameters such as RSRP, RSRQ, latency, and handover were measured. * An open-source dataset has been provided, which is publicly accessible at [18]. The dataset contains about six hours of aerial drive tests under different measurement scenarios such as routes, BS, BSs' heights, and UAV's height in a harsh tropical suburban environment. * Performance of the cellular-connected UAV system in the commercial LTE network has been investigated in a 3D form, under different distances and flight heights, in terms of RSRP and RSRQ. The statistical analysis reveals the performance of terrestrial BS in providing aerial coverage under different distances and flight heights. The output of this stage can be extended for other UAV mobile connectivity research, such as UAV path planning optimization. * Six ML-based models have been considered and evaluated for an accurate RSRP and RSRQ prediction for LTE networks in suburban environments. Multiple linear regression, polynomial, and logarithmic methods were utilized to estimate the level of RSRP and RSRQ based on the 2D distance between the drone and serving BS, elevation angle, and flight height. The rest of the paper is organized as follows:  
A review of recent related works is presented in Section 2. Section 3 describes the materials and methods used in this research, including measurement methodology, methods to analyze and assess the collected data, and the utilized method and steps toward developing RSRP and RSRQ prediction models. Measurement and simulation results are presented and discussed in Section 4. Finally, the paper ends with a conclusion in Section 5.