Causality-Aware Channel State Information Encoding

摘要: In Frequency Division Duplex (FDD) systems, for efficient communication, the downlink Channel State Information (CSI) should be sent to the base station through feedback links. Since such transmissions come with the cost of signaling overhead, the state-of-the-art has approaches for data-driven compression of CSI using auto-encoders and other Machine Learning (ML) algorithms. However, models built on a particular training dataset need additional domain transfer overhead for different test settings and environments. We propose a causality-aware Channel State Information (CSI) encoding system that adapts to changes in input data distribution as follows: (a) Create a model of the underlying constraints that generate the observational data, e.g., using Structural Causal Models (SCMs), where the model (e.g., SCMs) captures the cause-and-effect relationships between the observational or endogenous variable (i.e …