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Jul, 2022
双分图神经网络用于可扩展波束成形优化
A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization
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Junbeom Kim, Hoon Lee, Seung-Eun Hong, Seok-Hwan Park
TL;DR
该论文提出了一种双分图神经网络框架,用于多天线波束成形优化,可实现可扩展性,通过联合训练可以普遍适用于任意MU-MISO系统,并验证了其在传统方法上的优越性。
Abstract
deep learning
(DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (
mu-miso
) downlink systems owing to the capability of handling nonconvex formulations. H
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