Summary of Geometry Aware Meta-learning Neural Network For Joint Phase and Precoder Optimization in Ris, by Dahlia Devapriya and Sheetal Kalyani
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS
by Dahlia Devapriya, Sheetal Kalyani
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed complex-valued, geometry-aware meta-learning neural network optimizes the precoder matrix at the base station and phase shifts of reconfigurable intelligent surface (RIS) elements for multi-user multiple input single output systems. This joint optimization is typically complex, but leveraging Riemannian manifolds via complex circle geometry for phase shifts and spherical geometry for the precoder enables faster convergence. The approach combines a complex-valued neural network for phase shifts with an Euler-inspired update for the precoder network, outperforming existing neural network-based algorithms in terms of weighted sum rate, power consumption, and convergence speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new way to optimize communication systems using reconfigurable intelligent surfaces. They created a special kind of artificial intelligence called a meta-learning neural network that helps find the best combination of signals for different users. This approach is unique because it uses geometry from math to make the optimization process faster and more efficient. The results show that this method can improve communication rates, reduce power consumption, and speed up the process by nearly 100 times compared to other methods. |
Keywords
» Artificial intelligence » Meta learning » Neural network » Optimization
