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

TL;DR

This paper introduces a GNN-based algorithm for joint channel and power allocation in heterogeneous wireless networks, aiming to improve throughput and efficiency over traditional methods.

Contribution

It models the wireless network as a heterogeneous graph and proposes a novel GNN architecture for joint resource allocation, enhancing computational efficiency.

Findings

Achieves satisfactory throughput performance

Offers higher computational efficiency than traditional algorithms

Effectively models heterogeneous networks as graphs

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.