Embedding model-based fast meta learning for downlink beamforming adaptation

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta lea...

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Bibliographic Details

Main Authors:	Juping Zhang, Yi Yuan, Gan Zheng, Ioannis Krikidis, Kai-Kit Wong
Format:	Default Article
Published:	2021
Subjects:	Distributed computing and systems software not elsewhere classified Array signal processing Task analysis Adaptation models Neural networks Wireless communication Data models Complexity theory Meta learning Online learning Embedding model Beamforming Distributed Computing
Online Access:	https://hdl.handle.net/2134/19082402.v1
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