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

• URL: http://arxiv.org/abs/2307.08822v2
• Date: Tue, 3 Oct 2023 11:02:21 GMT
• Title: A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access
• Authors: Rafael Cerna Loli, Bruno Clerckx
• Abstract summary: 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
• Score: 53.191806757701215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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