JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels

Abstract:This paper considers channel state information (CSI) prediction using neural networks for time-varying multi-antenna channels from noisy historical observations. The main challenge is to exploit temporal correlation while distinguishing channel evolution from observation noise. We propose JEPA-kMMVAE, which combines a joint-embedding predictive architecture with a k-memory Markov variational autoencoder to predict future CSI through temporally structured latent representations. The framework supports direct prediction and residual corrections relative to the latest latent state and noisy CSI observation. Simulation results show that the residual configuration outperforms the evaluated baselines at moderate and high input CSI signal-to-noise ratios, while sequence baselines remain more accurate under stronger noise.

Signal Processing (eess.SP); Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2610.13315 [eess.SP]

(or arXiv:2610.13315v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2610.13315 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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