The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo é estudada a comunicação cooperativa distribuída de veículos aéreos não tripulados (VANTs), onde o número de condição (CN) e o produto interno (InP) são utilizados para medir a qualidade dos links de comunicação. Ao otimizar a posição relativa dos UAVs, é possível obter grande capacidade de canal e links de comunicação estáveis. Usando o modelo de onda esférica sob o canal de linha de visão (LOS), a expressão CN da matriz do canal é derivada quando há Nt transmissores e dois receptores no sistema. A fim de maximizar a capacidade do canal, derivamos a equação de restrição de posição dos UAVs (UAVs-PCE), e a restrição entre a distância dos elementos BS e o comprimento de onda da portadora é analisada. O resultado mostra que há uma área onde não importa como as posições dos UAVs sejam ajustadas, o CN ainda é muito grande. Em seguida, um cenário especial é considerado onde os UAVs formam uma rede retangular, e a restrição ideal entre a distância de comunicação e a distância dos UAVs é derivada. Depois disso, derivamos o InP da matriz do canal e a expressão gradiente do InP em relação à posição dos UAVs. O algoritmo de otimização por enxame de partículas (PSO) é usado para minimizar o CN e o algoritmo de descida gradiente (GD) é usado para minimizar o InP, otimizando a posição dos UAVs iterativamente. Ambos os algoritmos apresentam grande potencial para otimizar o CN e o InP respectivamente. Além disso, um algoritmo híbrido denominado PSO-GD combinando as vantagens dos dois algoritmos é proposto para maximizar a capacidade de comunicação com menor complexidade. Simulações mostram que PSO-GD é mais eficiente que PSO e GD. O PSO ajuda o GD a romper com o extremo local e fornece melhores posições para o GD, e o GD pode convergir rapidamente para uma solução ótima usando as informações de gradiente baseadas nas melhores posições. As simulações também revelam que um canal melhor pode ser obtido quando esses parâmetros satisfazem a equação de restrição de posição dos UAVs (UAVs-PCE), enquanto a análise teórica também explica os fenômenos anormais nas simulações.
Zhaoyang HOU
Xidian University
Zheng XIANG
Xidian University
Peng REN
Xidian University
Qiang HE
Xidian University
Ling ZHENG
Xi'an University of Post and Telecommunications
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Zhaoyang HOU, Zheng XIANG, Peng REN, Qiang HE, Ling ZHENG, "Distributed UAVs Placement Optimization for Cooperative Communication" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 6, pp. 675-685, June 2021, doi: 10.1587/transcom.2020EBP3117.
Abstract: In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3117/_p
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@ARTICLE{e104-b_6_675,
author={Zhaoyang HOU, Zheng XIANG, Peng REN, Qiang HE, Ling ZHENG, },
journal={IEICE TRANSACTIONS on Communications},
title={Distributed UAVs Placement Optimization for Cooperative Communication},
year={2021},
volume={E104-B},
number={6},
pages={675-685},
abstract={In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.},
keywords={},
doi={10.1587/transcom.2020EBP3117},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Distributed UAVs Placement Optimization for Cooperative Communication
T2 - IEICE TRANSACTIONS on Communications
SP - 675
EP - 685
AU - Zhaoyang HOU
AU - Zheng XIANG
AU - Peng REN
AU - Qiang HE
AU - Ling ZHENG
PY - 2021
DO - 10.1587/transcom.2020EBP3117
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2021
AB - In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.
ER -