AI 中文总结
针对流体天线系统获取信道信息及定位的障碍，提出基于联合嵌入预测架构的信道基础模型，通过提取潜在嵌入学习通用表示，结合互补损失项训练，经仿真验证其在信道外推和无线定位上优于传统基线。
AI 中文摘要
流体天线系统（FAS）是第六代（6G）无线网络的一项有前景的技术。它能在紧凑区域内让天线元件自由移动以利用丰富空间分集，但获取实时信道状态信息（CSI）、将信道值外推到未测量天线端口及确定准确用户位置存在障碍。本文引入基于联合嵌入预测架构（JEPA）的信道基础模型（CFM）。该模型用JEPA提取掩蔽或未观测信道段的高级潜在嵌入来学习通用表示，预训练目标结合三个互补损失项。预训练后冻结编码器，附加轻量级特定任务头用于信道外推和无线定位。仿真表明JEPA-CFM在信道外推和无线定位上显著优于传统掩蔽自动编码器基线。
英文摘要
Fluid antenna systems (FAS) have emerged as a promising technology for sixth-generation (6G) wireless networks. By allowing antenna elements to move freely within a compact region, FAS can exploit rich spatial diversity without additional hardware. However, acquiring real-time channel state information (CSI), extrapolating channel values to unmeasured antenna ports, and determining accurate user positions remain major obstacles. These challenges stem mainly from strong spatial correlations within the limited aperture and the scarcity of observable data. To overcome these limitations, this paper introduces joint embedding predictive architecture (JEPA)-based channel foundation model (CFM) specifically designed for FAS. The model adopts JEPA to learn versatile representations by extracting high-level latent embeddings of masked or unobserved channel segments. Unlike conventional approaches that attempt pixel-by-pixel reconstruction of raw CSI coefficients, JEPA-CFM focuses on predicting abstract structures in a compact feature space. The pre-training objective combines three complementary loss terms: the standard masked autoencoder reconstruction loss, the JEPA latent prediction loss, and a sliced isotropic Gaussian regularization (SIGReg) term. Together, these components prevent representation collapse and significantly enhance robustness under severe spatial correlation and highly sparse observations. After pre-training, the encoder is frozen, and lightweight task-specific heads are attached: a decoder for channel extrapolation and a global average pooling layer followed by a multi-layer perceptron regression head for wireless positioning. Extensive simulations in the realistic DeepMIMO urban scenario demonstrate that JEPA-CFM substantially outperforms the conventional masked autoencoder baseline in channel extrapolation and wireless positioning.
