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.
@article{arxiv.2307.08822,
title = {A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access},
author = {Rafael Cerna Loli and Bruno Clerckx},
journal= {arXiv preprint arXiv:2307.08822},
year = {2023}
}