A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access
This work addresses precoder optimization in wireless communication systems, offering a more efficient method for specific scenarios, but it appears incremental as it builds on existing meta-learning and RSMA techniques.
The paper tackled optimizing precoders for Rate-Splitting Multiple Access with partial channel state information by using a meta-learning framework that overfits a neural network to maximize average sum-rate, achieving similar performance to conventional methods in medium-scale scenarios and outperforming sub-optimal algorithms in large-scale cases.
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.