MIMO Channel as a Neural Function: Implicit Neural Representations for Extreme CSI Compression

Abstract

Acquiring and utilizing accurate channel state information (CSI) is crucial for realizing the benefits of massive multiple-input multiple-output (MIMO) technology. Current CSI feedback approaches improve precision by employing advanced deep-learning methods to learn representative CSI features for a subsequent compression process. Diverging from previous works, we treat the CSI compression problem in the context of implicit neural representations. Specifically, each CSI matrix is viewed as a neural function that maps the spatial coordinates (antenna and subchannel) to the corresponding channel gains with physical significance. Rather than transmitting the parameters of the specific neural functions directly, we send low-cost modulations of the CSI matrix, derived through a meta-learning algorithm. These modulations are then applied to a shared base network at the receiver to reconstruct the CSI matrix. Numerical results show that our proposed approach achieves state-of-the-art performance and showcases flexibility in feedback strategies.