In a vehicular scenario where the penetration of cars equipped with wireless communication devices is far from 100% and application requirements tend to be challenging for a cellular network not specifically planned for it, the use of unmanned aerial vehicles (UAVs), carrying mobile base stations, becomes an interesting option. In this article, we consider a cellular-vehicle-to-anything (C-V2X) application and we propose the integration of an aerial and a terrestrial component of the network, to fill the potential unavailability of short-range connections among vehicles and address unpredictable traffic distribution in space and time. In particular, we envision a UAV with C-V2X equipment providing service for the extended sensing application, and we propose a UAV trajectory design accounting for the radio resource (RR) assignment. The system is tested considering a realistic scenario by varying the RRs availability and the number of active vehicles. Simulations show the results in terms of gain in throughput and percentage of served users, with respect to the case in which the UAV is not present.

In recent years, interest in Unmanned Aerial Vehicles (UAVs) as a means to provide wireless connectivity has substantially increased thanks to their easy, fast and flexible deployment. Among the several possible applications of UAV networks explored by the current literature, they can be efficiently employed to collect Internet-of-Things (IoT) data because the non-stringent latency and small-size traffic type is particularly suited for UAVs’ inherent characteristics. However, the implications coming from the implementation of existing technology in such kinds of nodes are not straightforward. In this article, we consider a Narrow Band IoT (NB-IoT) network served by a UAV base station. Because of the many configurations possible within the NB-IoT standard, such as the access structure and numerology, we thoroughly review the technical aspects that have to be implemented and may be affected by the proposed UAV-aided IoT network. For proper remarks, we investigate the network performance jointly in terms of the number of successful transmissions, access rate, latency, throughput and energy consumption. Then, we compare the obtained results on different and known trajectories in the research community and study the impact of varying UAV parameters such as speed and height. Moreover, the numerical assessment allows us to extend the discussion to the potential implications of this model in different scenarios. Thus, this article summarizes all the main aspects that must be considered in planning NB-IoT networks with UAVs.

In this paper, we address the deployment of Unmanned Aerial Vehicles (UAVs) as Unmanned Aerial Base Stations (UABSs) which cooperate with Macro Base Stations (MBSs) in an urban environment to serve vehicles, denoted as Ground User Equipments (GUEs), implementing vehicle-to-everything (V2X) services. As vehicles perform extended sensing, exchanging data with nearby GUEs through UAVs and MBSs links, we propose an Integer Linear Programming (ILP) model that jointly optimizes radio resources allocation and beamforming, while accounting for vehicular application requirements, backhaul capacity limits and interference between GUE-UABS and GUE-MBS links. The model allows also to find a trade-off between benefits and cost of UABSs activation. Two system architectures are considered: a distributed model, where MBSs independently run the Radio Resource Management (RRM) algorithm sharing information with each other, and a centralized model, where MBSs send information to the network core, where the optimization algorithm runs. The study investigates interference through two resource allocation approaches, considering splitting and sharing of resources among UABSs and MBSs. Numerical evaluations demonstrate the effectiveness of using UABSs to improve the Quality of Experience (QoE) of GUEs. We also compare the two architectures, considering both resource pool assignments, and highlighting the impact of varying UABSs parameters and activation costs.

In recent years, the use of Unmanned Aerial Vehicles (UAVs) for wireless communications has been shown promising in a plethora of different applications. Their flexible deployment and mobility make them key enablers for the next generation of networks, provided that system design is properly addressed. In this article, we analyze a beyond-5G network where an Unmanned Aerial Vehicle (UAV), acting as Unmanned Aerial Base Station (UAB), is employed to collect data from Reduced Capability User Equipments (UEs), deployed in an urban area. Specifically, we study a cluster-based scenario, where UEs are deployed following a Thomas cluster process, and the UAB flies over cluster centers according to the Traveling Salesman Problem solution. Through the use of a stochastic approach, we mathematically devise the system performance accounting for uplink transmission protocol constraints, random access procedure, finite number of radio resources available, and coverage issues during the UAB flight. The mathematical model, validated via comparison with simulations, allows to optimize some system parameters, like the UAB speed, the number of UEs per cluster and the number of radio resources to be used for the access and for data transmissions.

In this paper, we study the potential advantages of integrating Unmanned Aerial Vehicles (UAVs) equipped with Base Stations, with a terrestrial mobile network infrastructure. In particular, our main focus here lays on handover and its role in B5G (Beyond 5G) networks. We consider a scenario where multiple UAVs and terrestrial bases jointly provide service to mobile ground users. By combining the UAV dynamic trajectory and radio access procedures, we investigate the impact and network performance of ground-to-air handovers. With respect to previous papers, we discuss how to optimise the UAV segment of the network. Final results show that the UAV needs to adjust its speed, height and antenna radiation angle to improve network performance.