Positioning of Multiple Unmanned Aerial Vehicle Base Stations in Future Wireless Network

Sivalingam, Thushan; Manosha, K. B. Shashika; Rajatheva, Nandana; Dissanayake, Maheshi B.

doi:10.1109/vtc2020-spring48590.2020.9129398

Cited by 10 publications

(13 citation statements)

References 15 publications

(15 reference statements)

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“…To demonstrate the accuracy and correctness, the proposed approach is validated against OLSR driven UAV assisted ground networks. The proposed approach is compared against software-defined UAVs (U-S) and UAVs as base stations (U-B) inspired by the configurations and settings in [ 28 , 29 ] for performance and efficiency analysis. The scalability of the proposed technique is verified by varying aerial node deployment and data rates.…”

Section: Resultsmentioning

confidence: 99%

“…The data rate of overall topology is managed to provide maximized throughput. Sivalingam [ 29 ] proposed that deploying multiple aerial nodes working as base stations can help uplift the coverage problem and maximize throughput. The authors have proposed an algorithm for determining locations within the pool of predetermined locations.…”

Section: Related Workmentioning

confidence: 99%

“…Section 4 provides a theoretical and mathematical description of the proposed approach. For performance analysis, the proposed approach is compared against software-defined UAVs (U-S) and UAVs as base stations (U-B) inspired by the configurations in [ 28 , 29 ], respectively. Section 5 also presents proof of correctness and scalability analysis of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Deployment with Throughput Maximization for UAVs Communication Networks

Sayeed

Kumar

Sharma

et al. 2020

Sensors

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The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

Sayeed

Kumar

Sharma

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…The papers form four groups according to their goals. In the first group, the authors aim to minimize the number of required FBSs [ 22 , 23 , 24 , 25 , 26 ], in the second, to achieve maximum coverage of UEs [ 26 , 27 , 28 , 29 , 30 ], in the third, to maximize the network throughput [ 31 , 32 ], and, in the fourth, to maximize the spectral efficiency [ 33 ]. The optimization problem is either solved by existing algorithms or their combination [ 24 , 27 , 28 , 32 , 33 ] or a new algorithm is developed or derived from a previous algorithm [ 22 , 23 , 25 , 29 , 31 ].…”

Section: Literature Review and State Of The Art Discussionmentioning

confidence: 99%

Modeling Optimal Location Distribution for Deployment of Flying Base Stations as On-Demand Connectivity Enablers in Real-World Scenarios

Pokorny

Šeda

et al. 2021

Sensors

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The amount of internet traffic generated during mass public events is significantly growing in a way that requires methods to increase the overall performance of the wireless network service. Recently, legacy methods in form of mobile cell sites, frequently called cells on wheels, were used. However, modern technologies are allowing the use of unmanned aerial vehicles (UAV) as a platform for network service extension instead of ground-based techniques. This results in the development of flying base stations (FBS) where the number of deployed FBSs depends on the demanded network capacity and specific user requirements. Large-scale events, such as outdoor music festivals or sporting competitions, requiring deployment of more than one FBS need a method to optimally distribute these aerial vehicles to achieve high capacity and minimize the cost. In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of FBSs by finding their optimal locations. It is restricted by users’ throughput requirements and FBSs’ available throughput, also, all users that require connectivity must be served. Two meta-heuristic algorithms (cuckoo search and differential evolution) were implemented and verified on a real example of a music festival scenario. The results show that both algorithms are capable of finding a solution. The major difference is in the performance where differential evolution solves the problem six to eight times faster, thus it is more suitable for repetitive calculation. The obtained results can be used in commercial scenarios similar to the one used in this paper where providing sufficient connectivity is crucial for good user experience. The designed algorithms will serve for the network infrastructure design and for assessing the costs and feasibility of the use-case.

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“…In [10], [11], the authors have analysed the Doppler effect on the air-to-ground channel and the methods to mitigate the same. Several works like [12]- [19] have considered UAV deployment as the aerial base station and considered coverage analysis, optimal altitude deployment, user association etc. However, resource allocation and energy efficiency for UAV communications have not been considered.…”

Section: Introductionmentioning

confidence: 99%

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

et al. 2021

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Applications of Unmanned aerial vehicles (UAVs) have advanced rapidly in recent years. The UAVs are used for a variety of applications, including surveillance, disaster management, precision agriculture, weather forecasting, etc. In near future, the growing number of UAV applications would necessitate densification of UAV infrastructure (ground radio station (GRS) and ground control station (GCS)) at the expense of increased energy consumption for UAV communications. Maximizing the energy efficiency of this UAV infrastructure is important. Motivated by this, we propose joint resource allocation and UAV scheduling with GRS sleeping. Further, we propose the use of coordinated multipoint (CoMP) with joint transmission (JT) and non-orthogonal multiple access (NOMA) along with GRS sleeping to increase the coverage and data rates, respectively. We then present exhaustive simulation results showcasing the trade-off between throughput and energy efficiency with varying UAV densities. We also compare the coverage, throughput, and energy efficiency for NOMA, CoMP with JT and the benchmark scenario.INDEX TERMS Coordinated multi-point (CoMP), ground control station (GCS), ground radio station sleeping (GRSS), non-orthogonal multiple access (NOMA), unmanned aerial vehicle (UAV).

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Positioning of Multiple Unmanned Aerial Vehicle Base Stations in Future Wireless Network

Cited by 10 publications

References 15 publications

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

Modeling Optimal Location Distribution for Deployment of Flying Base Stations as On-Demand Connectivity Enablers in Real-World Scenarios

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

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