Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming

Xiao, Zhenyu; Dong, Hang; Bai, Lin; Wu, Dapeng; Xia, Xiang-Gen

doi:10.1109/jiot.2019.2954620

“…When transceivers or BSs are mounted on flying UAVs, they are known as aerial BSs, Drone-BSs, or UAV-BSs [73]. These kinds of UAVs are expected to form flying cells and satisfy the growing data demands of users [74]. UAV-BSs can extend the capacity of the next-generation cellular networks (i.e., 5G, B5G, and 6G) due to their flexible mobility, their rapid deployability, and their LoS communication links [75].…”

Section: ) Uav Base Station (Uav-bs)mentioning

confidence: 99%

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

Oubbati

¹

,

Atiquzzaman

²

,

Ahanger

³

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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“…In this section, we introduce the method to solve Lagrangian problem (15) as well as adjusting the Lagrangian multipliers to deduce a good feasible solution to the original problem (12). A two-loop optimization framework is developed, where the inner-loop optimizes the Lagrangian problem (15) for given Lagrangian multipliers, and the outer-loop updates the multipliers to decrease the duality gap.…”

Section: Solution Of the Lagrangian Problemmentioning

confidence: 99%

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Yi¹,

Zhu²,

Zhu³

et al. 2021

Preprint

Self Cite

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<div>In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we fifirst model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confifirm that the proposed solution signifificantly outperforms two benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.</div>

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“…For any given Lagrangian multipliers, the optimal solution for Lagrangian problem ( 15) is an upper bound on original problem ( 12) [24]. To decrease the duality gap and obtain a feasible solution to original problem (12), the Lagrangian (15). multipliers need to be adjusted.…”

Section: B Lagrangian Relaxationmentioning

confidence: 99%

“…To decrease the duality gap between Lagrangian problem (15) and original problem (12), the Lagrangian multipliers need to be adjusted. This is actually a dual problem with respect to Lagrangian multipliers, which can be optimized by the gradient method [31].…”

Section: Updating Lagrangian Multipliersmentioning

confidence: 99%

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Yi¹,

Li²,

Zhu³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

<div>In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we fifirst model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confifirm that the proposed solution signifificantly outperforms two benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.</div>

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Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming

Cited by 142 publications

References 41 publications

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

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

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

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