Joint Aerial-Terrestrial Resource Management in UAV-Aided Mobile Radio Networks

Verdone, Roberto; Mignardi, Silvia

doi:10.1109/mnet.2018.1800036

Cited by 31 publications

(12 citation statements)

References 13 publications

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“…Verdone et al [229] have optimized the uplink data rate of GUs and energy-efficiency of UAVs. This is done by adopting an SDN-based architecture, which considers issues related to a joint radio resource management between the GCS and UAVs.…”

Section: E Sdn-based Cellular Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: E Sdn-based Cellular Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…In [26], how radio-maps can drive UAVs is studied, in order to exploit the environment-dependent path loss in a specific area and take advantage of a better coverage. In our previous works [27,28] we considered issues related to a joint radio resource management (RRM) between the terrestrial base stations and a UAV. However, we had a uniform scenario with a large number of active users; different applications with different demands may require algorithm adaptation and simpler and more practical considerations.…”

Section: Related Workmentioning

confidence: 99%

“…We assumed that the UAV knew: (1) the position of all vehicles that are under cellular coverage in each time instant and their application requirements; and (2) the pool of RR available to it and the set of RRs used by the TBSs. This can be possible by centralizing these network operations in a network entity that manages both TBSs and UAV through Software Defined Networking (SDN) and network function virtualization (NFV) techniques [27]. Then, the UAV was responsible for: (1) defining its mission and trajectory in real-time; and (2) RR assignment at its side.…”

Section: System Modelmentioning

confidence: 99%

Trajectories and Resource Management of Flying Base Stations for C-V2X

Mignardi

Buratti

Bazzi

et al. 2019

Sensors

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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.

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“…The authors in [8] propose a trajectory optimization scheme in which the time required for a cellular-connected UAV to reach its destination is minimized. In [9], the authors study joint aerial-terrestrial resource management in UAV-assisted mobile networks. To mitigate interference, a learning based approach is proposed in [10].…”

Section: Introductionmentioning

confidence: 99%