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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Shrnutí: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 maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3329036