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Resource allocation in heterogeneous network with node and edge enhanced graph attention network. / Сунь, Цюши; Хе, Ян; Петросян, Ованес Леонович.

In: Applied Intelligence, Vol. 54, No. 6, 01.03.2024, p. 4865–4877.

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@article{8d4131943ecd4d66830f127848f4058f,
title = "Resource allocation in heterogeneous network with node and edge enhanced graph attention network",
abstract = "In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex challenges in wireless communications and have shown promising results. However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). Specifically designed for heterogeneous networks, HNENN incorporates node-level and edge-level attention layers to learn and aggregate node and edge embeddings. The alternating stacking of these two layers facilitates the mutual enhancement of node and edge embeddings. Simulations show that the proposed framework works better than state-of-the-art approaches, getting a higher sum rate in different scenarios with different numbers of D2D pairs, training samples, interference levels, and transmit power budgets.",
keywords = "Edge enhancement, Graph attention Network, Heterogeneous Network, Resource allocation",
author = "Цюши Сунь and Ян Хе and Петросян, {Ованес Леонович}",
year = "2024",
month = mar,
day = "1",
doi = "10.1007/s10489-024-05391-4",
language = "English",
volume = "54",
pages = "4865–4877",
journal = "Applied Intelligence",
issn = "0924-669X",
publisher = "Springer Nature",
number = "6",

}

RIS

TY - JOUR

T1 - Resource allocation in heterogeneous network with node and edge enhanced graph attention network

AU - Сунь, Цюши

AU - Хе, Ян

AU - Петросян, Ованес Леонович

PY - 2024/3/1

Y1 - 2024/3/1

N2 - In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex challenges in wireless communications and have shown promising results. However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). Specifically designed for heterogeneous networks, HNENN incorporates node-level and edge-level attention layers to learn and aggregate node and edge embeddings. The alternating stacking of these two layers facilitates the mutual enhancement of node and edge embeddings. Simulations show that the proposed framework works better than state-of-the-art approaches, getting a higher sum rate in different scenarios with different numbers of D2D pairs, training samples, interference levels, and transmit power budgets.

AB - In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex challenges in wireless communications and have shown promising results. However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). Specifically designed for heterogeneous networks, HNENN incorporates node-level and edge-level attention layers to learn and aggregate node and edge embeddings. The alternating stacking of these two layers facilitates the mutual enhancement of node and edge embeddings. Simulations show that the proposed framework works better than state-of-the-art approaches, getting a higher sum rate in different scenarios with different numbers of D2D pairs, training samples, interference levels, and transmit power budgets.

KW - Edge enhancement

KW - Graph attention Network

KW - Heterogeneous Network

KW - Resource allocation

UR - https://www.mendeley.com/catalogue/bf72a870-88dc-3c12-9905-30b87eb2af8e/

U2 - 10.1007/s10489-024-05391-4

DO - 10.1007/s10489-024-05391-4

M3 - Article

VL - 54

SP - 4865

EP - 4877

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 6

ER -

ID: 126141249