GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks
This work addresses resource allocation for improved interference, data rates, and energy efficiency in wireless networks, but it appears incremental as it applies an existing GNN approach to a specific domain problem.
The paper tackled the joint channel and power allocation problem in heterogeneous wireless networks to maximize network throughput, proposing a GNN-based algorithm that achieved satisfactory performance with higher computational efficiency compared to traditional optimization methods.
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.