Core Concepts
Utilizing neural functions for CSI compression in massive MIMO systems improves performance and reduces communication overhead.
Abstract
The article discusses the use of implicit neural representations for extreme CSI compression in massive MIMO systems. It introduces a novel approach treating CSI matrices as neural functions, leading to state-of-the-art performance and flexibility in feedback strategies. The content covers system models, DL-based compression techniques, INR-based compression schemes, and training strategies. It also explores quantization and entropy coding effects on performance.
Structure:
- Introduction to Massive MIMO Technology
- Problem Overview: System Model and DL-based Compression
- CSI with Implicit Neural Representations: Architecture of CSI-INR Scheme
- Training and Evaluation: Experimental Setup and Performance Analysis
Stats
"Our proposed approach achieves state-of-the-art performance."
"Numerical results show that our proposed model yields significant performance enhancements compared to existing CNN or transformer-based methodologies."
Quotes
"Recent developments in neural compression, combined with the observed correlations between the INR model and CSI data, motivate the utilization of INR for CSI compression."
"Through incorporating diverse scales and shifts across multiple intermediate SIREN layers, our modulated SIREN layers enable the parameterization of various CSI data points within an ensemble of neural networks."