  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.
