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GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks by Li, Yuhang; Lu, Yang; Ai, Bo; Dobre, Octavia A.; Ding, Zhiguo; Niyato, Dusit is a scholarly article available to read on EtoBox.

What is GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks about?

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. By modeling the MU-MISO network as a graph, a GNN-based architecture named CRGAT is proposed to directly map the channel state information to the beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and avoid the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the base station. The CRGAT is trained in an unsupervised learning manner with two proposed loss functions. An evaluation method is proposed for the learning-based approach, 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 incl

Author

Li, Yuhang; Lu, Yang; Ai, Bo; Dobre, Octavia A.; Ding, Zhiguo; Niyato, Dusit

Published

2023

Language

EN