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
본 논문에서는 조건수(CN)와 내적(InP)을 사용하여 통신 링크의 품질을 측정하는 무인 항공기(UAV)의 분산 협력 통신에 대해 연구합니다. UAV의 상대적 위치를 최적화함으로써 큰 채널 용량과 안정적인 통신 링크를 얻을 수 있습니다. LOS(Line of Sight) 채널 아래 구형파 모델을 사용하여 다음과 같은 경우 채널 행렬의 CN 표현이 파생됩니다. Nt 시스템의 송신기와 두 개의 수신기. 채널 용량을 최대화하기 위해 UAV 위치 제약 방정식(UAVs-PCE)을 도출하고 기지국 요소 거리와 반송파 파장 간의 제약을 분석합니다. 결과는 UAV의 위치를 어떻게 조정하더라도 CN이 여전히 매우 큰 영역이 있음을 보여줍니다. 그런 다음 UAV가 직사각형 격자 배열을 형성하는 특수 시나리오를 고려하고 통신 거리와 UAV 거리 간의 최적 제약 조건을 도출합니다. 그 후, 채널 행렬의 InP와 UAV 위치에 대한 InP의 기울기 표현을 유도합니다. CN을 최소화하기 위해 PSO(Particle Swarm Optimization) 알고리즘을 사용하고 UAV의 위치를 반복적으로 최적화하여 InP를 최소화하기 위해 Gradient Descent(GD) 알고리즘을 사용합니다. 두 알고리즘 모두 각각 CN과 InP를 최적화할 수 있는 큰 잠재력을 제시합니다. 또한, 두 알고리즘의 장점을 결합한 PSO-GD라는 하이브리드 알고리즘을 제안하여 복잡성을 낮추고 통신 용량을 극대화합니다. 시뮬레이션에서는 PSO-GD가 PSO 및 GD보다 더 효율적이라는 것을 보여줍니다. PSO는 GD가 국소 극값에서 벗어나도록 도와주고 GD에게 더 나은 위치를 제공하며, GD는 더 나은 위치를 기반으로 한 그래디언트 정보를 활용하여 신속하게 최적의 솔루션으로 수렴할 수 있습니다. 또한 시뮬레이션을 통해 해당 매개변수가 UAV 위치 제약 방정식(UAVs-PCE)을 충족할 때 더 나은 채널을 얻을 수 있음이 밝혀졌으며, 이론 분석도 시뮬레이션의 비정상적인 현상을 설명합니다.
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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부
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
부
@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},}
부
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 -