AI Empowered Resource Management for Future Wireless Networks

TL;DR

This paper explores AI-based resource management in wireless networks, highlighting advantages, comparing neural network architectures, and providing theoretical insights into their performance, especially favoring graph neural networks for interference management.

Contribution

It identifies key advantages of AI methods, proposes optimality gap as a selection metric, and theoretically compares GNNs and MLPs for interference management.

Findings

AI methods offer four main advantages over classical techniques.

Optimality gap helps in selecting neural network architectures.

GNNs outperform MLPs, with the gap growing as √K.

Abstract

Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning techniques, has recently emerged as a disruptive technology to solve such challenging problems in a real-time manner. However, although promising results have been reported, practical design guidelines and performance guarantees of AI-based approaches are still missing. In this paper, we endeavor to address two fundamental questions: 1) What are the main advantages of AI-based methods compared with classical techniques; and 2) Which neural network should we choose for a given resource management task. For the first question, four advantages are identified and discussed. For the second question, \emph{optimality gap}, i.e., the gap to the optimal performance, is proposed as a measure for selecting model architectures, as well as, for enabling a theoretical comparison between different AI-based approaches. Specifically, for $K$-user interference management problem, we theoretically show that graph neural networks (GNNs) are superior to multi-layer perceptrons (MLPs), and the performance gap between these two methods grows with $\sqrt{K}$.