AI 中文总结
UniCSI提出统一基础架构，通过物理信息RF分词器和频谱聚合器直接处理异构CSI，在25个数据集上实现跨域迁移性能提升。
AI 中文摘要
Wi-Fi感知有望将我们周围已有的日常无线信号转化为用于人体感知的普适传感器。然而，一个根本性障碍在于，CSI是在多种设备特定配置下获取的，包括不同的子载波数量、带宽和载波频段。因此，生成的CSI张量在频谱分辨率和张量形状上均存在差异，使得异构建模颇具挑战。标准架构难以应对这种异构性，不得不采用有损预处理，从而损害了底层信号。为弥合这一差距，我们提出了UniCSI，一种统一的基础架构，可直接处理异构CSI，同时保持原生波形的完整性。UniCSI依赖于两项核心创新：（1）一种物理信息驱动的RF分词器，它根据每个频率通道在物理频谱中的分数位置（而非固定的数组索引）对其进行编码。该分词器保留了固有的频谱相干性，并支持在任意感知配置下进行无缝的、与频率分辨率无关的处理。（2）一种频谱聚合器，将可变长度的通道序列提炼为固定大小的频谱特征，从而有效地将特征维度与物理子载波间距解耦。在包含25个异构公开数据集的大规模语料库上进行的大量评估（涵盖14至2048个子载波、20至160 MHz带宽以及2.4 GHz和5 GHz频段）表明，在监督和自监督训练方案下，原生异构数据输入显著改善了跨域迁移，尤其是在固定网格架构无法泛化的场景中。
英文摘要
Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental obstacle is that CSI is acquired under diverse device-specific configurations, including different subcarrier counts, bandwidths, and carrier bands. Consequently, the resulting CSI tensors vary in both spectral resolution and tensor shape, making heterogeneous modeling challenging. Standard architectures struggle with such heterogeneity, forcing lossy pre-processing which compromises the underlying signal. To bridge this gap, we present UniCSI, a unified foundation architecture that directly operates on heterogeneous CSI while preserving the integrity of the native waveform. UniCSI hinges on two core innovations: (1) a physics-informed RF tokenizer that encodes each frequency channel based on its fractional position within the physical spectrum rather than rigid array indices. It preserves intrinsic spectral coherence and enables seamless, frequency resolution-agnostic processing across arbitrary sensing configurations. (2) a spectral aggregator that distills variable-length channel sequences into a fixed-size spectral signature, effectively decoupling the feature dimensionality from the physical subcarrier spacing. Extensive evaluations on a large-scale corpus of 25 heterogeneous public datasets, spanning 14 to 2048 subcarriers, 20 to 160 MHz bandwidth, and the 2.4 and 5 GHz bands, demonstrate that native heterogeneous ingestion substantially improves cross-domain transfer under both supervised and self-supervised training schemes, particularly in regimes where fixed-grid architectures fail to generalize.
