Causality-Aware Channel State Information Encoding

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., channel state information) and the unobserved or exogenous variables (e.g., User Equipment (UE) speed, frequency, etc.). The causal graph is represented by directed acyclic graphs (DAGs), where nodes (vertices) correspond to the endogenous variables, and the directed edges account for the causal parent-child relationship. In this scenario, the endogenous variable is the input vector of vectors, H, whereas the exogenous variables include the vendor and non-vendor specific parameters, e.g., power thresholds, channel quality indicator, etc., as detailed in the document, and (b) Perform domain adaptation by applying the learned Structural Causal Model to the received data, e.g., by using a causal layer in the neural network. To the best of our knowledge, this work first demonstrates the efficacy of causality-aware CSI compression and its usefulness in domain adaptability, out-of-distribution generalization, and power savings.