GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the user...

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Published in:	IEEE transactions on wireless communications Vol. 23; no. 8; pp. 9251 - 9264
Main Authors:	Li, Yuhang, Lu, Yang, Ai, Bo, Dobre, Octavia A., Ding, Zhiguo, Niyato, Dusit
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Array signal processing Beamforming Budgets Convexity CRGAT GNNs Graph neural networks Interference Machine learning Maximization Message passing MISO communication MU-MISO Optimization Quality of service Signal processing algorithms State vectors sum-rate maximization Topology Unsupervised learning Wireless networks
ISSN:	1536-1276, 1558-2248
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Abstract	The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station (BS). By modeling the MU-MISO network as a graph, a GNN-based architecture named complex residual graph attention network (CRGAT) is proposed to directly map channel state information to beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and mitigate the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the BS. The CRGAT is trained via unsupervised learning with two proposed loss functions. An evaluation method is proposed for the learning-based approaches, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings.
AbstractList	The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users' individual data rate requirements and the power budget of the base station (BS). By modeling the MU-MISO network as a graph, a GNN-based architecture named complex residual graph attention network (CRGAT) is proposed to directly map channel state information to beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and mitigate the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the BS. The CRGAT is trained via unsupervised learning with two proposed loss functions. An evaluation method is proposed for the learning-based approaches, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings.
Author	Dobre, Octavia A. Lu, Yang Li, Yuhang Ding, Zhiguo Ai, Bo Niyato, Dusit
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SubjectTerms	Array signal processing Beamforming Budgets Convexity CRGAT GNNs Graph neural networks Interference Machine learning Maximization Message passing MISO communication MU-MISO Optimization Quality of service Signal processing algorithms State vectors sum-rate maximization Topology Unsupervised learning Wireless networks
Title	GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks
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