Particle Swarm Optimization Algorithms for Altitude and Transmit Power Adjustments in UAV-Assisted Cellular Networks

Shukla, Shourya; Thakur, Rahul; Agarwal, Swati

doi:10.1109/vtc2021-spring51267.2021.9448994

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Moreover, a backhaul scheme was proposed to solve the extra interference caused by the wireless backhaul link in [21]. Different from the existing works that analyzed altitudes of UAVs in the performance analysis of the network, the positions of UAVs in UAV-assisted wireless networks were optimized in [22,23], and the transmission power of UAVs was analyzed in [24]. In the studies reviewed above, the number of UAVs is generally fixed, and the randomness of locations of them is seldom considered, hence the emerging need for the deployment of large-scale UAVs to satisfy the needs of network capacity in hot spot scenarios.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Performance Analysis in UAV-Assisted Cellular Networks with Clustered Edge Users

Yao

Zhu

et al. 2022

Electronics

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A UAV-assisted cellular network can provide ubiquitous links to everything and it is considered to be one of the key technologies for 6G wireless networks. In this paper, we consider an uplink wireless network with a macrobase station (MBS) and cellular users. However, the coverage equality of edge users cannot be guaranteed in scenarios where data service is dense. Specifically, a novel topology of the UAV-assisted wireless network is considered. UAVs are deployed upon the cell edge to serve edge users with poor communication quality. To avoid larger interference caused by users and UAVs in the overlapping area, the locations of these UAVs are modeled as a homogeneous Poisson point process (HPPP) under the Poisson cluster distance constraint (PCDC). In addition, we assume that edge users cluster around each UAV and model their locations as Poisson cluster processes (PCPs). Initially, the Laplace transforms of intra-cluster interference, inter-cluster interference, and other interference are derived. Subsequently, coverage probability and area spectrum efficiency are derived for UAVs and MBS using tools from stochastic geometry. Moreover, the energy efficiency of the system is obtained. Simulation results are examined to validate the accuracy of theoretical analysis and provide insights into the effects of the system parameters as well as useful guidelines for practical system design.

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Section: Related Workmentioning

confidence: 99%

Modeling and Performance Analysis in UAV-Assisted Cellular Networks with Clustered Edge Users

Yao

Zhu

et al. 2022

Electronics

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“…According to this success story, UAV-BS has become a key carrier to provide beyond 5G networks (B5G). Unlike traditional GBSs, the UAV-BS networks are adaptive in multiple parameters, such as altitude, and transmission power, three-dimensional (3D) location [4] [5]. The deployment of UAV-BS is very flexible under any unrealistic conditions or time constraints on the ground [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

Lai¹,

Bhola²,

Tsai³

et al. 2022

Preprint

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Unmanned aerial vehicle-aided communication (UAB-BS) is a promising solution to establish rapid wireless connectivity in sudden/temporary crowded events because of its more flexibility and mobility features than conventional ground base station (GBS). Because of these benefits, UAV-BSs can easily be deployed at high altitudes to provide more line of sight (LoS) links than GBS. Therefore, users on the ground can obtain more reliable wireless channels. In practice, the mobile nature of the ground user can create uneven user density at different times and spaces. This phenomenon leads to unbalanced user associations among UAV-BSs and may cause frequent UAV-BS overload. We propose a three-dimensional adaptive and fair deployment approach to solve this problem. The proposed approach can jointly optimize the altitude and transmission power of UAV-BS to offload the traffic from overloaded UAV-BSs. The simulation results show that the network performance improves by 37.71% in total capacity, 37.48% in total energy efficiency and 16.12% in the Jain fairness index compared to the straightforward greedy approach.

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Multiobjective Optimization for Improving Throughput and Energy Efficiency in UAV-Enabled IoT

Liu

Wang

Sun

et al. 2022

IEEE Internet Things J.

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Particle Swarm Optimization Algorithms for Altitude and Transmit Power Adjustments in UAV-Assisted Cellular Networks

Cited by 6 publications

References 28 publications

Modeling and Performance Analysis in UAV-Assisted Cellular Networks with Clustered Edge Users

Modeling and Performance Analysis in UAV-Assisted Cellular Networks with Clustered Edge Users

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

Multiobjective Optimization for Improving Throughput and Energy Efficiency in UAV-Enabled IoT

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