GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks

• URL: http://arxiv.org/abs/2408.03957v1
• Date: Sun, 28 Jul 2024 04:51:00 GMT
• Title: GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks
• Authors: Lili Chen, Jingge Zhu, Jamie Evans,
• Abstract summary: This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Our proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.
• Score: 9.031738020845586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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