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- W4385096291 abstract "Power control for the device-to-device interference channel with single-antenna transceivers has been widely analyzed with both model-based methods and learning-based approaches. Although the learning-based approaches, i.e., datadriven and model-driven, offer performance improvement, the widely adopted graph neural network suffers from learning the heterophilous power distribution of the interference channel. In this paper, we propose a deep learning architecture in the family of graph transformers to circumvent the issue. Experiment results show that the proposed methods achieve the state-of-theart performance across a wide range of untrained network configurations. Furthermore, we show there is a trade-off between model complexity and generality." @default.
- W4385096291 created "2023-07-23" @default.
- W4385096291 creator A5025467937 @default.
- W4385096291 creator A5042130024 @default.
- W4385096291 date "2023-07-19" @default.
- W4385096291 modified "2023-10-16" @default.
- W4385096291 title "Power Allocation for Device-to-Device Interference Channel Using Truncated Graph Transformers" @default.
- W4385096291 doi "https://doi.org/10.48550/arxiv.2307.10538" @default.
- W4385096291 hasPublicationYear "2023" @default.
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