Optimum UAV positioning for better coverage-connectivity tradeoff

Abdel-Malek, Mai A.; Ibrahim, Ahmed S.; Mokhtar, Mohamed

doi:10.1109/pimrc.2017.8292633

Cited by 18 publications

(7 citation statements)

References 8 publications

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“…It reflects the formation status of MAPs and is used as a criteria in the mode switching. The connectivity of the MAP network can be measured in terms of its Fiedler value (i.e., the secondsmallest eigenvalue of the Laplacian matrix L) [16]. The adjacency matrix A = [a i,j ] is defined as:…”

Section: B Coverage and Connectivitymentioning

confidence: 99%

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Wang¹,

Farooq²

2022

Preprint

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Unmanned aerial vehicles (UAVs) are a convenient choice for carrying mobile base stations to rapidly setup communication services for ground users. Unlike terrestrial networks, UAVs do not have fiber optic backhaul connectivity except when they are tethered to the ground, which restricts their mobility. In the absence of back-haul, e.g., in remote areas, emergency situations, or in battlefields, there is a need to ensure connectivity among UAVs in addition to coverage of ground users for creating local area networks. This paper provides a distributed and dynamic approach for UAV formationbased control for coverage and connectivity of spatially dispersed users. We use flocking dynamics as a guide to constructing tailored formations of UAVs on the fly. Simulation results demonstrate that if sufficient aerial base stations are available, the proposed approach results in a strongly connected network of UAVs that is able to provide both a backhaul and fronthaul network. The approach can be further extended to create multi-tier extra-terrestrial networks to cater for large-scale applications.

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Section: B Coverage and Connectivitymentioning

confidence: 99%

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Wang¹,

Farooq²

2022

Preprint

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“…5 Each ABS shares the corresponding entry in x f with its neighbors N i . 6 Each ABS i needs to compute ∂ap,q ∂xi for its neighbors as in (14). 7 Each ABS i computes its moving directions based on (13).…”

Section: Energy Consumption and Maximum Flow Trade-offmentioning

confidence: 99%

“…In [12], the effectiveness of ABS deployment in overload and outage situations is investigated where the relay placement, the number of relays, and the relay transmit power are analyzed. In [14]- [17], the connectivity of the ABS network is characterized by its Fiedler value and algebraic connectivity. However, their approach is different from ours, as they consider neither the cognitive radio setting nor dynamic interference avoidance.…”

mentioning

confidence: 99%

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Rahmati

Güvenç

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

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“…The access link performance gains of using UAVs have been studied extensively in the literature for public safety [15], [16], device-to-device communications [17], [18], Internet of Things (IoT) applications [19], [20], smart cities [21], non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) networks [22], [23], coverage [24]- [26], connectivity [27]- [29] and capacity maximization [30], [31]. Furthermore, 3GPP has been investigating the integration of UAVs into existing cellular networks [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

et al. 2019

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An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of nextgeneration cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of UAVs. We consider two spatial configuration modes of UAVs: distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs cannot only be used for coverage improvement but also for capacity boosting in IAB cellular networks.Index Terms-3D localization, downlink, drone, drone antenna array (DAA), in-band, integrated access and backhaul (IAB), optimization, UAV.

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Optimum UAV positioning for better coverage-connectivity tradeoff

Cited by 18 publications

References 8 publications

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

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