arXiv cs.AI· Yuzhi Yang, Brahim Mefgouda, Hang Zou, Lina Bariah, Anis Bara, Yuhuan Lu, Hao Zhang, Merouane Debbah·· 3 小时前AI 评分33
受 JEPA 启发的隐空间场预测用于稀疏导频 MIMO-OFDM 信道重建
JEPA-Inspired Latent Field Prediction for Sparse-Pilot MIMO-OFDM Channel Reconstruction
AI 导读
一篇 arXiv 论文提出受 JEPA 启发的场世界模型，将无线传播场建模为隐空间预测模型，用于稀疏导频下的 MIMO-OFDM 信道重建。该方法把 CSI 观测映射为隐空间时频 token，从历史多频段上下文预测被遮蔽或未来的隐空间场状态，并与稀疏含噪导频融合以重建完整信道。Ray-tracing 实验显示其 NMSE 优于仅导频和直接重建基线。
正文 · 原文
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.
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37391 [eess.SP] |
| (or arXiv:2610.37391v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37391 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Brahim Mefgouda [view email]
[v1]
Tue, 27 Oct 2026 15:14:41 UTC (10,059 KB)
来源：arXiv cs.AI · arxiv.org