Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks

TL;DR

This paper introduces a graph neural network-based method, JCPGNN-M, for joint channel and power allocation in multi-channel wireless networks, improving data rates and scalability over traditional algorithms.

Contribution

The paper presents JCPGNN-M, a novel GNN-based solution that supports multi-channel allocation per user and enforces power constraints via a Lagrangian framework, enhancing efficiency and scalability.

Findings

JCPGNN-M outperforms eWMMSE in data rate.

JCPGNN-M has significantly lower inference time.

JCPGNN-M generalizes well to larger networks.

Abstract

As the number of mobile devices continues to grow, interference has become a major bottleneck in improving data rates in wireless networks. Efficient joint channel and power allocation (JCPA) is crucial for managing interference. In this paper, we first propose an enhanced WMMSE (eWMMSE) algorithm to solve the JCPA problem in multi-channel wireless networks. To reduce the computational complexity of iterative optimization, we further introduce JCPGNN-M, a graph neural network-based solution that enables simultaneous multi-channel allocation for each user. We reformulate the problem as a Lagrangian function, which allows us to enforce the total power constraints systematically. Our solution involves combining this Lagrangian framework with GNNs and iteratively updating the Lagrange multipliers and resource allocation scheme. Unlike existing GNN-based methods that limit each user to a single channel, JCPGNN-M supports efficient spectrum reuse and scales well in dense network scenarios. Simulation results show that JCPGNN-M achieves better data rate compared to eWMMSE. Meanwhile, the inference time of JCPGNN-M is much lower than eWMMS, and it can generalize well to larger networks.