AI Insight
This paper presents a novel approach for reconstructing complete wireless channel information in MIMO-OFDM systems using sparse pilot signals. The method employs a joint-embedding predictive architecture (JEPA) that works in a latent representation space rather than directly on raw channel measurements, predicting missing channel states by learning the underlying propagation field patterns across time and frequency domains. Ray-tracing simulations demonstrate improved reconstruction accuracy compared to conventional pilot-only and direct reconstruction methods.
Why it matters
This approach could significantly reduce the overhead required for channel estimation in 5G and future wireless systems, potentially improving spectral efficiency and network capacity. By requiring fewer pilot signals while maintaining accurate channel reconstruction, the method could enable more efficient use of wireless spectrum and support higher data rates in MIMO-OFDM communication systems.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Accurate acquisition of full channel state information (CSI) is essential for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems, while dense pilot-based estimation incurs substantial overhead. Directly minimizing the prediction error over raw CSI coefficients is fragile in realistic propagation environments, since small-scale fading, mobility, local scattering, and small spatial perturbations can induce large phase-sensitive variations. Nevertheless, wireless channels contain strong spatial, temporal, and frequency-domain correlations that can be more predictably represented in a latent propagation-field domain. Motivated by this observation, we propose a joint-embedding predictive architecture (JEPA)-inspired field world model, defined as a latent predictive model of the wireless propagation field, for sparse-pilot channel reconstruction. The model maps CSI observations into latent time-frequency tokens, predicts masked or future latent field states from historical multi-band context, and fuses the predicted latent prior with sparse noisy current pilots to reconstruct the full MIMO-OFDM channel. Ray-tracing experiments show that the proposed method improves normalized mean square error (NMSE) over pilot-only and direct reconstruction baselines, supporting latent field prediction as an effective prior for sparse-pilot channel acquisition.
Source: JEPA-Inspired Latent Field Prediction for Sparse-Pilot MIMO-OFDM Channel Reconstruction