ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks
This addresses the need for efficient real-time resource allocation in wireless networks, representing an incremental improvement by extending GNN capabilities to handle edge variables.
The authors tackled the problem of low-latency radio resource management in wireless networks by proposing a GNN architecture with an edge-update mechanism, achieving higher sum rates and significantly shorter computation times than state-of-the-art methods.
In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.