Context-Enhanced CSI Tracking Using Koopman-Inspired Dual Autoencoders in Dynamic Wireless Environments

Abstract

This paper introduces a novel framework for tracking and predicting Channel State Information (CSI) by leveraging Physics-Informed Autoencoders (PIAE) integrated with a learned Koopman operator. The proposed approach models CSI as a nonlinear dynamical system governed by both intrinsic channel behavior and exogenous contextual factors such as position, temperature, and atmospheric conditions. The architecture comprises dual autoencoders-one dedicated to CSI and another to contextual inputs-linked via a shared latent state space, within which the Koopman operator captures the linear temporal evolution of CSI dynamics. This coupling enables accurate, data-driven forecasting of CSI trajectories while maintaining interpretability through a structured, physics-consistent representation. The framework supports real-time updates to the Channel Knowledge Map (CKM), enhancing the adaptability and reliability of communication systems in complex and time-varying environments. By unifying Koopman theory with learned latent representations, the proposed method provides a scalable and privacy-preserving solution for next-generation wireless networks. Empirical results demonstrate its effectiveness in delivering high-fidelity CSI predictions under diverse channel conditions.