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.

翻译：本文提出采用基于元学习的预编码器优化框架，在发射端仅掌握部分信道状态信息（CSIT）的条件下，直接优化速率分割多址接入（RSMA）预编码器。通过利用紧凑型神经网络的过拟合特性最大化显式平均和速率（ASR）表达式，我们有效避免了对外部训练数据的需求，同时将总运行时间降至最低。数值结果表明，在中规模场景下，基于元学习的解决方案能够达到与传统预编码器优化相近的ASR性能，而在大规模场景下，其性能显著优于次优的低复杂度预编码算法。