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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Vydané v:	IEEE transactions on wireless communications Ročník 23; číslo 8; s. 9251 - 9264
Hlavní autori:	Li, Yuhang, Lu, Yang, Ai, Bo, Dobre, Octavia A., Ding, Zhiguo, Niyato, Dusit
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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