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

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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.

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by Saint Petersburg State University (project ID:94062114).

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Authors and Affiliations

  1. Faculty of Applied Mathematics and Control Processes, St. Petersburg State University, Universitetsky prosp., 2, St. Petersburg, 198504, Russia

    Qiushi Sun

  2. Faculty of Applied Mathematics and Control Processes, St. Petersburg State University, Universitetsky prosp., 2, St. Petersburg, 198504, Russia

    Yang He & Ovanes Petrosian

Authors
  1. Qiushi Sun
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  2. Yang He
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  3. Ovanes Petrosian
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Contributions

Qiushi Sun carried out the investigation, methodology, and formal analysis, participated in the Validation, and drafted the manuscript. Ovanes Petrosian participated in the study design and performed the statistical analysis. Yang He conceived the study, participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ovanes Petrosian.

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Sun, Q., He, Y. & Petrosian, O. Resource allocation in heterogeneous network with node and edge enhanced graph attention network. Appl Intell 54, 4865–4877 (2024). https://doi.org/10.1007/s10489-024-05391-4

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  • DOI: https://doi.org/10.1007/s10489-024-05391-4

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