Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization
Unmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV place...
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doaj-d51e23294d424fa69e3acee30928e97c2021-09-02T23:00:37ZengIEEEIEEE Access2169-35362021-01-01911968411969910.1109/ACCESS.2021.31076749521913Multi-Tier Variable Height UAV Networks: User Coverage and Throughput OptimizationMuhammad Nafees0https://orcid.org/0000-0003-3191-1803John Thompson1Majid Safari2https://orcid.org/0000-0001-7777-0052School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, U.KSchool of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, U.KSchool of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, U.KUnmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative. This paper presents a novel variable height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as BSs. We propose a multi-tier variable height UAV-based network deployment and compare its performance with the state-of-the-art equal height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. The results show that our proposed method is more energy-efficient in a multi-cell UAV network than the most widely used height optimization method in the literature. In UAV networks, users at the cell edges can receive very poor signal-to-interference-plus-noise ratio (SINR) levels due to interfering UAVs. To cope with this problem, we adopt a fractional frequency reuse (FFR) scheme to compensate low SINR levels. We optimize the SINR threshold corresponding to each cell to maximize their spectral efficiency (SE), thereby improving the network’s area spectral efficiency (ASE). The numerical results show that the proposed deployments provide significant gains in coverage density, SINR coverage probability, rate coverage, and ASE compared to equal height benchmark scheme. As the number of UAVs increases, the number of tiers need to increase to preserve the rate coverage of the network. Moreover, the performance of the proposed variable height model is expected to converge to that of equal height cellular design for a large number of UAVs.https://ieeexplore.ieee.org/document/9521913/Unmanned aerial vehicles (UAVs)signal-to-interference-plus-noise ratio (SINR)fractional frequency reuse (FFR)area spectral efficiency (ASE)reinforcement learning (RL) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Muhammad Nafees John Thompson Majid Safari |
spellingShingle |
Muhammad Nafees John Thompson Majid Safari Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization IEEE Access Unmanned aerial vehicles (UAVs) signal-to-interference-plus-noise ratio (SINR) fractional frequency reuse (FFR) area spectral efficiency (ASE) reinforcement learning (RL) |
author_facet |
Muhammad Nafees John Thompson Majid Safari |
author_sort |
Muhammad Nafees |
title |
Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization |
title_short |
Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization |
title_full |
Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization |
title_fullStr |
Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization |
title_full_unstemmed |
Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization |
title_sort |
multi-tier variable height uav networks: user coverage and throughput optimization |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Unmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative. This paper presents a novel variable height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as BSs. We propose a multi-tier variable height UAV-based network deployment and compare its performance with the state-of-the-art equal height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. The results show that our proposed method is more energy-efficient in a multi-cell UAV network than the most widely used height optimization method in the literature. In UAV networks, users at the cell edges can receive very poor signal-to-interference-plus-noise ratio (SINR) levels due to interfering UAVs. To cope with this problem, we adopt a fractional frequency reuse (FFR) scheme to compensate low SINR levels. We optimize the SINR threshold corresponding to each cell to maximize their spectral efficiency (SE), thereby improving the network’s area spectral efficiency (ASE). The numerical results show that the proposed deployments provide significant gains in coverage density, SINR coverage probability, rate coverage, and ASE compared to equal height benchmark scheme. As the number of UAVs increases, the number of tiers need to increase to preserve the rate coverage of the network. Moreover, the performance of the proposed variable height model is expected to converge to that of equal height cellular design for a large number of UAVs. |
topic |
Unmanned aerial vehicles (UAVs) signal-to-interference-plus-noise ratio (SINR) fractional frequency reuse (FFR) area spectral efficiency (ASE) reinforcement learning (RL) |
url |
https://ieeexplore.ieee.org/document/9521913/ |
work_keys_str_mv |
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