Generalizable CSI Feedback with Physics-Based Deep Learning

This paper addresses the critical issue of generalizability in deep learning-based CSI feedback for massive MIMO systems. The authors tackle the problem of performance degradation in unseen environments by incorporating physics-based principles into the learning process. This approach is significant because it aims to reduce deployment costs by creating models that are robust across different channel conditions. The proposed EG-CsiNet framework, along with the physics-based distribution alignment, is a novel contribution that could significantly improve the practical applicability of deep learning in wireless communication.

• •Addresses the generalizability problem of deep learning-based CSI feedback in unseen environments.
• •Proposes a physics-based distribution alignment approach to improve robustness.
• •Introduces EG-CsiNet, a novel learning framework.
• •Demonstrates significant performance improvements over existing methods in simulations and sim-to-real experiments.