A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximi...

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Vydané v:	IEEE wireless communications letters Ročník 13; číslo 2; s. 347 - 351
Hlavní autori:	Cerna Loli, Rafael, Clerckx, Bruno
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Channel state information Complexity theory Decoding Interference Learning Metalearning Multiple access Neural networks non-convex optimization Optimization partial channel state information at the transmitter (CSIT) Rate-splitting multiple access (RSMA) Splitting
ISSN:	2162-2337, 2162-2345
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