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.

本文提出了一种基于元学习的预编码器优化框架，用于直接优化具有部分发射机信道状态信息的速率分割多址（RSMA）预编码器。通过利用紧凑神经网络的过拟合来最大化明确的平均和速率表达式，我们有效规避了对任何其他训练数据的需求，同时最小化了总运行时间。数值结果表明，在中等规模场景中，基于元学习的解决方案可以达到与传统预编码器优化相似的和速率性能，在大规模情况下明显优于次优的低复杂度预编码器算法。

一种基于元学习的速率分裂多址预编码优化框架