Contents I. Introduction
Commercial drone applications have attracted profound interest in recent years in a wide set of use-cases, including area monitoring, surveillance, and delivery [1] . In many applications, drones, also known as unmanned aerial vehicles (UAVs), require connectivity to carry their tasks out. Due to the ubiquitous coverage of cellular networks, they serve as the major infrastructure for providing wide-area yet reliable and secure drone connectivity especially for beyond visual line-of-sight range [2] , [3] . A comprehensive set of empirical analyses on providing connectivity for drones through LTE networks have been conducted recently in the context of 3GPP, and some of field-test results could be found in [4] . Regarding the fact that the probability of experiencing line-of-sight (LoS) propagation to the neighbor BSs increases with altitude [5] , the wireless channels between flying users and neighboring base stations (BSs) experience almost free-space fading [4] . Hence, in the uplink direction, drone communications are expected to incur significant interference to uplink communications of terrestrial UEs, and in the downlink communications, drones are vulnerable to receive strong interference from neighbour BSs, as shown in Fig. 1 [6] . Furthermore, due to the mobility of drones in a 3-dimensional (3D) space without predetermined roads as compared to the legacy terrestrial UEs, radio resource provisioning in the sky becomes a difficult task in this dynamic environment in comparison to the legacy urban/rural service areas with predetermined buildings and roads. Moreover, the terrestrial users usually receive strong signals from a few neighbor BSs. This makes the user-BS association problem less complicated in comparison with the drone communications in which, drones observe LoS signals from several BSs. By considering the speed of drones and the large-set of potential BSs that a drone can be served by, excessive number of handover events will be triggered for drones [7] . Then, introduction of aerial users to cellular networks needs a revision in communication technologies which have been developed for legacy terrestrial users. Here, we focus on the handover and radio resource management (H-RRM) problem in serving drone communications. In this problem, the key performance indicators of our interest are: (i) the interference of drone communications on terrestrial communications, and (ii) the experienced delay in drone communications. Among candidate enablers for solving such a complex and dynamic problem, we leverage machine learning (ML) tools, transform the problem into a machine learning problem, and provide a solution for it. The proposed ML schemes enable cellular networks to capture the temporal and spatial correlations between decisions taken in the network in serving drones to make a foresightful and cognitive decision in later decision epochs. To the best of authors’ knowledge, our work is the first in literature that investigates the HRRM problem in a network consisting of drone and terrestrial users, and leverages an ML-powered solution for solving the problem. The key contributions of this work include:
Analyze the received interference from drone communications in the ground BSs using cell planning tools and leveraging real geographical and land-use data.
Formulate the H-RRM problem in serving drone and terrestrial users as a machine learning problem by incorporating delay in drone communications and interference to terrestrial users in the design of the reward function.
Present a reinforcement learning solution to the problem.
Present the impacts of different system parameters on the H-RRM problem decisions. Analyze the interplay between interference to terrestrial users, handover overhead, allocated resources to drones, and experienced delay.
Present boundaries of cells in the sky, handover regions, as a function of altitude and speed of drones, and level of tolerable interference to the terrestrial users.
