Tiny Graph Neural Networks for Radio Resource Management
<|reference_start|>Tiny Graph Neural Networks for Radio Resource Management: The surge in demand for efficient radio resource management has necessitated the development of sophisticated yet compact neural network architectures. In this paper, we introduce a novel approach to Graph Neural Networks (GNNs) tailored for radio resource management by presenting a new architecture: the Low Rank Message Passing Graph Neural Network (LR-MPGNN). The cornerstone of LR-MPGNN is the implementation of a low-rank approximation technique that substitutes the conventional linear layers with their low-rank counterparts. This innovative design significantly reduces the model size and the number of parameters. We evaluate the performance of the proposed LR-MPGNN model based on several key metrics: model size, number of parameters, weighted sum rate of the communication system, and the distribution of eigenvalues of weight matrices. Our extensive evaluations demonstrate that the LR-MPGNN model achieves a sixtyfold decrease in model size, and the number of model parameters can be reduced by up to 98%. Performance-wise, the LR-MPGNN demonstrates robustness with a marginal 2% reduction in the best-case scenario in the normalized weighted sum rate compared to the original MPGNN model. Additionally, the distribution of eigenvalues of the weight matrices in the LR-MPGNN model is more uniform and spans a wider range, suggesting a strategic redistribution of weights.<|reference_end|>
@article{ghasemi2024tiny,
    title={Tiny Graph Neural Networks for Radio Resource Management},
    author={Ahmad Ghasemi, Hossein Pishro-Nik},
    journal={arXiv preprint arXiv:2403.19143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19143},
    primaryClass={cs.LG cs.NI eess.SP}
}