TY - JOUR

T1 - Graph attention network enhanced power allocation for wireless cellular system

AU - Qiushi, S.

AU - Хе, Ян

AU - Petrosyan, O.

N1 - S. Qiushi, H. Yang, O. Petrosian, “Graph attention network enhanced power allocation for wireless cellular system”, Informatics and Automation, 23:1 (2024), 259–283

PY - 2024/1/11

Y1 - 2024/1/11

N2 - The importance of an efficient network resource allocation strategy has grown significantly with the rapid advancement of cellular network technology and the widespread use of mobile devices. Efficient resource allocation is crucial for enhancing user services and optimizing network performance. The primary objective is to optimize the power distribution method to maximize the total aggregate rate for all customers within the network. In recent years, graph-based deep learning approaches have shown great promise in addressing the challenge of network resource allocation. Graph neural networks (GNNs) have particularly excelled in handling graph-structured data, benefiting from the inherent topological characteristics of mobile networks. However, many of these methodologies tend to focus predominantly on node characteristics during the learning phase, occasionally overlooking or oversimplifying the importance of edge attributes, which are equally vital as nodes in network modeling. To tackle this limitation, we introduce a novel framework known as the Heterogeneous Edge Feature Enhanced Graph Attention Network (HEGAT). This framework establishes a direct connection between the evolving network topology and the optimal power distribution strategy throughout the learning process. Our proposed HEGAT approach exhibits improved performance and demonstrates significant generalization capabilities, as evidenced by extensive simulation results.

AB - The importance of an efficient network resource allocation strategy has grown significantly with the rapid advancement of cellular network technology and the widespread use of mobile devices. Efficient resource allocation is crucial for enhancing user services and optimizing network performance. The primary objective is to optimize the power distribution method to maximize the total aggregate rate for all customers within the network. In recent years, graph-based deep learning approaches have shown great promise in addressing the challenge of network resource allocation. Graph neural networks (GNNs) have particularly excelled in handling graph-structured data, benefiting from the inherent topological characteristics of mobile networks. However, many of these methodologies tend to focus predominantly on node characteristics during the learning phase, occasionally overlooking or oversimplifying the importance of edge attributes, which are equally vital as nodes in network modeling. To tackle this limitation, we introduce a novel framework known as the Heterogeneous Edge Feature Enhanced Graph Attention Network (HEGAT). This framework establishes a direct connection between the evolving network topology and the optimal power distribution strategy throughout the learning process. Our proposed HEGAT approach exhibits improved performance and demonstrates significant generalization capabilities, as evidenced by extensive simulation results.

KW - MISO

KW - cellular network

KW - edge-feature

KW - graph attention network

KW - power allocation

UR - https://www.mendeley.com/catalogue/3c138bd3-7af9-3f05-a242-d02f726e5676/

U2 - 10.15622/ia.23.1.9

DO - 10.15622/ia.23.1.9

M3 - Article

VL - 23

SP - 259

EP - 283

JO - SPIIRAS Proceedings

JF - SPIIRAS Proceedings

SN - 2078-9181

IS - 1

ER -