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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Bibliographic Details
Published in:IEEE wireless communications letters Vol. 13; no. 2; pp. 347 - 351
Main Authors: Cerna Loli, Rafael, Clerckx, Bruno
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2023.3329036