Graph Neural Networks for Wireless Communications: From Theory to
Practice
- URL: http://arxiv.org/abs/2203.10800v1
- Date: Mon, 21 Mar 2022 08:39:44 GMT
- Title: Graph Neural Networks for Wireless Communications: From Theory to
Practice
- Authors: Yifei Shen, Jun Zhang, S.H. Song, Khaled B. Letaief
- Abstract summary: Graph neural networks (GNNs) can effectively exploit the domain knowledge, i.e., the graph topology in wireless communication problems.
For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures.
For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks.
- Score: 10.61745503150249
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning-based approaches have been developed to solve challenging
problems in wireless communications, leading to promising results. Early
attempts adopted neural network architectures inherited from applications such
as computer vision. They often require huge amounts of training samples (i.e.,
poor generalization), and yield poor performance in large-scale networks (i.e.,
poor scalability). To resolve these issues, graph neural networks (GNNs) have
been recently adopted, as they can effectively exploit the domain knowledge,
i.e., the graph topology in wireless communication problems. GNN-based methods
can achieve near-optimal performance in large-scale networks and generalize
well under different system settings, but the theoretical underpinnings and
design guidelines remain elusive, which may hinder their practical
implementations. This paper endeavors to fill both the theoretical and
practical gaps. For theoretical guarantees, we prove that GNNs achieve
near-optimal performance in wireless networks with much fewer training samples
than traditional neural architectures. Specifically, to solve an optimization
problem on an $n$-node graph (where the nodes may represent users, base
stations, or antennas), GNNs' generalization error and required number of
training samples are $\mathcal{O}(n)$ and $\mathcal{O}(n^2)$ times lower than
the unstructured multi-layer perceptrons. For design guidelines, we propose a
unified framework that is applicable to general design problems in wireless
networks, which includes graph modeling, neural architecture design, and
theory-guided performance enhancement. Extensive simulations, which cover a
variety of important problems and network settings, verify our theory and
effectiveness of the proposed design framework.
Related papers
- DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
Graph neural networks are recognized for their strong performance across various applications.
BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks.
We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
arXiv Detail & Related papers (2024-06-04T07:24:51Z) - FERN: Leveraging Graph Attention Networks for Failure Evaluation and
Robust Network Design [46.302926845889694]
We develop a learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.
FERN represents rich problem inputs as a graph and captures both local and global views by attentively performing feature extraction from the graph.
It can speed up multiple robust network design problems by more than 80x, 200x, 10x, respectively with negligible performance gap.
arXiv Detail & Related papers (2023-05-30T15:56:25Z) - Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural
Networks [29.23937571816269]
We propose an edge-graph-neural-network (Edge-GNN) to learn the cooperative beamforming on the graph edges.
The proposed Edge-GNN achieves higher sum rate with much shorter computation time than state-of-the-art approaches.
arXiv Detail & Related papers (2022-11-23T02:05:06Z) - GNN at the Edge: Cost-Efficient Graph Neural Network Processing over
Distributed Edge Servers [24.109721494781592]
Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions.
This paper studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network.
We show that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.
arXiv Detail & Related papers (2022-10-31T13:03:16Z) - Scaling Graph-based Deep Learning models to larger networks [2.946140899052065]
Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.
This paper presents a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.
arXiv Detail & Related papers (2021-10-04T09:04:19Z) - Binary Graph Neural Networks [69.51765073772226]
Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data.
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
arXiv Detail & Related papers (2020-12-31T18:48:58Z) - Graph Neural Networks for Scalable Radio Resource Management:
Architecture Design and Theoretical Analysis [31.372548374969387]
We propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems.
The proposed method is highly scalable and can solve the beamforming problem in an interference channel with $1000$ transceiver pairs within $6$ milliseconds on a single GPU.
arXiv Detail & Related papers (2020-07-15T11:43:32Z) - Optimization and Generalization Analysis of Transduction through
Gradient Boosting and Application to Multi-scale Graph Neural Networks [60.22494363676747]
It is known that the current graph neural networks (GNNs) are difficult to make themselves deep due to the problem known as over-smoothing.
Multi-scale GNNs are a promising approach for mitigating the over-smoothing problem.
We derive the optimization and generalization guarantees of transductive learning algorithms that include multi-scale GNNs.
arXiv Detail & Related papers (2020-06-15T17:06:17Z) - Towards an Efficient and General Framework of Robust Training for Graph
Neural Networks [96.93500886136532]
Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks.
Despite GNNs' impressive performance, it has been observed that carefully crafted perturbations on graph structures lead them to make wrong predictions.
We propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs.
arXiv Detail & Related papers (2020-02-25T15:17:58Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.