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：本文提出一种基于元学习的预编码优化框架，用于在发射端仅具备部分信道状态信息的条件下，直接优化速率分割多址接入的预编码器。通过利用紧凑神经网络对显式平均和速率表达式的过拟合特性，该方法在无需额外训练数据的同时，有效缩短了总运行时间。数值结果表明，在中规模场景下，基于元学习的方案与传统预编码优化方法具有相近的平均和速率性能，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。