HMM-Based CSI Embedding for Trajectory Recovery via Feature Engineering on MIMO-OFDM Channels in LOS/NLOS Regions

Constructing channel state information (CSI) maps is essential for advancing wireless communications and localization. However, building CSI maps in practice is hindered by the need for up-to-date, location-labeled CSI measurement data. Conventional CSI embedding methods typically project CSI into a low-dimensional latent space, which may lack clear physical interpretability for localization tasks. This paper addresses the problem of extracting user locations from CSI measurements and recovering user trajectories in indoor multiple-input multiple-output (MIMO)-Orthogonal Frequency-Division Multiplexing (OFDM) communication scenarios. We develop a hidden Markov model (HMM) framework and propose an alternating optimization algorithm to jointly learn model parameters and recover user trajectories in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments. Proof-of-concept experiments are conducted using ray-tracing data from a uniform linear antenna array (ULA) antenna setup, achieving an average localization error of 0.87 meters. The results demonstrate the potential of the proposed approach for practical indoor CSI map construction.