# A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access

arXiv: https://arxiv.org/abs/2307.08822 · HTML: https://mlsanity.com/paper/2307.08822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization](https://mlsanity.com/paper/2009.04899/) (2020)
- [Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis](https://mlsanity.com/paper/1906.04474/) (2019)
- [Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel with Partial CSIT: A Rate-Splitting Approach](https://mlsanity.com/paper/1912.05409/) (2019)
- [A matrix-inverse-free implementation of the MU-MIMO WMMSE beamforming algorithm](https://mlsanity.com/paper/2205.08877/) (2022)
- [A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming](https://mlsanity.com/paper/2206.10499/) (2022)
- [A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming](https://mlsanity.com/paper/2210.13279/) (2022)
- [Rate-Splitting Multiple Access for Multi-Antenna Broadcast Channels with Statistical CSIT](https://mlsanity.com/paper/2104.00220/) (2021)

## Built on by (most cited first, 1 of 1)

- [Meta-Learning Based Optimization for Large Scale Wireless Systems](https://mlsanity.com/paper/2407.01823/) (2024)

## Abstract (arXiv)

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.