Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929201610752000 |
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| author | Chen, Lili Zhu, Jingge Evans, Jamie |
| author_facet | Chen, Lili Zhu, Jingge Evans, Jamie |
| contents | Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph representation method for wireless networks that include full-duplex (FD) nodes. We then design a corresponding FD Graph Neural Network (F-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our F-GNN achieves state-of-art performance with significantly less computation time. Besides, F-GNN offers an excellent trade-off between performance and complexity compared to classical approaches. We further refine this trade-off by introducing a distance-based threshold for inclusion or exclusion of edges in the network. We show that an appropriately chosen threshold reduces required training time by roughly 20% with a relatively minor loss in performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_16113 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes Chen, Lili Zhu, Jingge Evans, Jamie Networking and Internet Architecture Information Theory Machine Learning Signal Processing Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph representation method for wireless networks that include full-duplex (FD) nodes. We then design a corresponding FD Graph Neural Network (F-GNN) with the aim of allocating transmit powers to maximise the network throughput. Our results show that our F-GNN achieves state-of-art performance with significantly less computation time. Besides, F-GNN offers an excellent trade-off between performance and complexity compared to classical approaches. We further refine this trade-off by introducing a distance-based threshold for inclusion or exclusion of edges in the network. We show that an appropriately chosen threshold reduces required training time by roughly 20% with a relatively minor loss in performance. |
| title | Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes |
| topic | Networking and Internet Architecture Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2303.16113 |