一种基于JEPA的场层世界模型，用于衔接信道预测与估计
A JEPA-Based Field-Layer World Model for Bridging Channel Prediction and Estimation
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中文总结 AI 辅助
该研究针对MIMO-OFDM无线系统中CSI预测鲁棒性差的问题，提出基于JEPA的场层世界模型，通过多尺度对齐策略实现跨频段重构，显著提升了波束成形增益。
中文摘要 AI 辅助
信道状态信息（CSI）的获取、重构与预测是现代MIMO-OFDM无线系统中基础但成本高昂的任务。原始CSI的直接系数级预测在实际传播环境中鲁棒性差，因为微小的空间扰动、局部散射变化和相位波动会在复信道域引发大幅误差。然而，底层的无线传播场仍包含可跨时间、频率、天线和载波维度利用的稳定且可预测的结构。受此观察启发，我们提出一种基于JEPA的场层世界模型（FWM），该模型从考虑的载波频段内多分辨率CSI观测中学习共享的隐式传播状态，并在隐式域中预测其与任务相关的演化。所提FWM通过特定尺度的分词器头将多个CSI观测分辨率映射到共享的隐式传播场空间。随后训练隐式预测主干以推断被掩码或未来的场状态，而增量多尺度对齐策略允许在不从头重新训练整个模型的情况下纳入新的观测尺度。对于下游重构，预测的隐式场被用作结构化先验，并与稀疏的当前导频结合。在单频段和跨频段重构上的实验表明，尽管NMSE改善幅度不大，但符号检测性能得到提升，更显著的是波束成形增益大幅提高，这表明FWM捕捉到了超出系数级CSI拟合的、与任务相关的空间传播结构。
英文摘要
Channel state information (CSI) acquisition, reconstruction, and prediction are fundamental yet costly tasks in modern MIMO-OFDM wireless systems. Direct coefficient-level prediction of raw CSI is fragile in realistic propagation environments, since small spatial perturbations, local scattering changes, and phase variations can cause large errors in the complex channel domain. However, the underlying wireless propagation field still contains stable and predictable structures that can be exploited across time, frequency, antenna, and carrier dimensions. Motivated by this observation, we propose a JEPA-based field-layer world model (FWM) that learns a shared latent propagation state from multi-resolution CSI observations across the considered carrier bands and predicts its task-relevant evolution in the latent domain. The proposed FWM maps multiple CSI observation resolutions to a shared latent propagation-field space through scale-specific tokenizer heads. A latent prediction backbone is then trained to infer masked or future field states, while an incremental multi-scale alignment strategy allows new observation scales to be incorporated without retraining the entire model from scratch. For downstream reconstruction, the predicted latent field is used as a structured prior and combined with sparse current pilots. Experiments on single-band and cross-band reconstruction demonstrate improved symbol detection and, more notably, substantial beamforming gains despite modest NMSE improvements, indicating that FWM captures task-relevant spatial propagation structure beyond coefficient-wise CSI fitting.
