Transformer-Empowered Predictive Beamforming for Rate-Splitting Multiple Access in Non-Terrestrial Networks

Existing Rate-Splitting Multiple Access (RSMA) techniques offer a promise for Non-Terrestrial Networks (NTNs) by managing interference and ensuring reliable data transmission. However, precoder design remains a crucial bottleneck, demanding accurate Channel State Information (CSI) feedback and compl...

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Vydané v:	IEEE transactions on wireless communications Ročník 23; číslo 12; s. 19776 - 19788
Hlavní autori:	Zhang, Shengyu, Zhang, Shiyao, Yuan, Weijie, Quek, Tony Q. S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Array signal processing Artificial neural networks Beamforming Complexity Convolutional neural networks Data transmission Design optimization Feedback Harnesses Interference Low earth orbit satellites Machine learning Multiple access Non-terrestrial networks (NTNs) predictive beamforming Predictive models Protocols rate-splitting multiple access (RSMA) Resource management Satellites Splitting Transformers
ISSN:	1536-1276, 1558-2248
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