# Meta-Learning Based Optimization for Large Scale Wireless Systems

arXiv: https://arxiv.org/abs/2407.01823 · HTML: https://mlsanity.com/paper/2407.01823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 50 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2007.00038/) (2020)
- [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)
- [Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution](https://mlsanity.com/paper/1603.01217/) (2016)
- [Rate-Splitting Multiple Access for Multi-antenna Downlink Communication Systems: Spectral and Energy Efficiency Tradeoff](https://mlsanity.com/paper/2001.03206/) (2020)
- [Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis](https://mlsanity.com/paper/2011.11362/) (2020)
- [Beyond Diagonal Reconfigurable Intelligent Surfaces: From Transmitting and Reflecting Modes to Single-, Group-, and Fully-Connected Architectures](https://mlsanity.com/paper/2205.02866/) (2022)
- [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)
- [Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook](https://mlsanity.com/paper/2401.01433/) (2024)
- [Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications](https://mlsanity.com/paper/2103.07914/) (2021)
- [Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2205.00849/) (2022)
- [Rate-Splitting Multiple Access for 6G -- Part III: Interplay with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.02036/) (2022)
- [A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming](https://mlsanity.com/paper/2210.13279/) (2022)
- [Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff](https://mlsanity.com/paper/2101.11957/) (2021)
- [A Rate-Splitting Strategy to Enable Joint Radar Sensing and Communication with Partial CSIT](https://mlsanity.com/paper/2105.00633/) (2021)
- [Rate-Splitting Multiple Access for 6G -- Part II: Interplay with Integrated Sensing and Communications](https://mlsanity.com/paper/2205.02462/) (2022)
- [Rate-Splitting Multiple Access for 6G -- Part I: Principles, Applications and Future Works](https://mlsanity.com/paper/2205.02548/) (2022)
- [Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging](https://mlsanity.com/paper/2302.06853/) (2023)
- [Synergizing Beyond Diagonal Reconfigurable Intelligent Surface and Rate-Splitting Multiple Access](https://mlsanity.com/paper/2303.06912/) (2023)

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

- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)

## Abstract (arXiv)

Optimization algorithms for wireless systems play a fundamental role in improving their performance and efficiency. However, it is known that the complexity of conventional optimization algorithms in the literature often exponentially increases with the number of transmit antennas and communication users in the wireless system. Therefore, in the large scale regime, the astronomically large complexity of these optimization algorithms prohibits their use and prevents assessing large scale wireless systems performance under optimized conditions. To overcome this limitation, this work proposes instead the use of an unsupervised meta-learning based approach to directly perform non-convex optimization at significantly reduced complexity. To demonstrate the effectiveness of the proposed meta-learning based solution, the sum-rate (SR) maximization problem for the following three emerging 6G technologies is contemplated: hierarchical rate-splitting multiple access (H-RSMA), integrated sensing and communication (ISAC), and beyond-diagonal reconfigurable intelligent surfaces (BD-RIS). Through numerical results, it is demonstrated that the proposed meta-learning based optimization framework is able to successfully optimize the performance and also reveal unknown aspects of the operation in the large scale regime for the considered three 6G technologies.