Problem
Research questions and friction points this paper is trying to address.
CSI prediction
time-varying channels
noisy observations
temporal correlation
multi-antenna
JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels
🤖 AI Summary
This study addresses the challenge of predicting channel state information (CSI) from noisy historical observations in time-varying multi-antenna channels, with a focus on effectively decoupling channel evolution dynamics from noise. To this end, it proposes a novel framework that integrates the Joint Embedding Predictive Architecture (JEPA) with a k-memory Markov Variational Autoencoder (kMMVAE) to construct temporally structured latent representations for future CSI prediction. The key contribution lies in designing both a direct prediction scheme and a residual correction mechanism conditioned on the latest latent state and noisy observations. Experimental results demonstrate that the residual configuration significantly outperforms baseline methods under moderate-to-high signal-to-noise ratios, whereas sequence-based baselines achieve superior accuracy in strongly noisy scenarios.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
JEPA-kMMVAE
CSI Prediction
Joint-Embedding Predictive Architecture
Markov Variational Autoencoder
Residual Correction
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