GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks
<|reference_start|>GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks: The optimal allocation of channels and power resources plays a crucial role in ensuring minimal interference, maximal data rates, and efficient energy utilisation. As a successful approach for tackling resource management problems in wireless networks, Graph Neural Networks (GNNs) have attracted a lot of attention. This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Concretely, we model the heterogeneous wireless network as a heterogeneous graph and then propose a graph neural network structure intending to allocate the available channels and transmit power to maximise the network throughput. Our proposed joint channel and power allocation graph neural network (JCPGNN) comprises a shared message computation layer and two task-specific layers, with a dedicated focus on channel and power allocation tasks, respectively. Comprehensive experiments demonstrate that the proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.<|reference_end|>
@article{chen2024gnn-based,
    title={GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless
  Networks},
    author={Lili Chen, Jingge Zhu and Jamie Evans},
    journal={arXiv preprint arXiv:2408.03957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03957},
    primaryClass={cs.NI cs.IT cs.LG eess.SP math.IT}
}