  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.
