arXiv：cs.LG· Daniel Eckhoff, Hua Kang, Zhitang Chen, Jie Chuai·· 4 小时前AI 评分39
UniCSI：面向泛在人体感知的通用 Wi-Fi CSI 编码器
UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing
AI 导读
研究团队提出 UniCSI，一个可直接处理异构 CSI 的统一基础架构，保留原始波形完整性。其核心包括基于物理频谱分数位置编码的 RF tokenizer 和将变长信道序列压缩为固定长度频谱特征的 spectral aggregator，实现与频率分辨率无关的处理。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) |
| MSC classes: | 68T07, 94A12 |
| ACM classes: | I.2.6; I.5.4; C.2.1 |
| Cite as: | arXiv:2610.09559 [cs.LG] |
| (or arXiv:2610.09559v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09559 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Daniel Eckhoff [view email]
[v1]
Wed, 7 Oct 2026 07:00:57 UTC (536 KB)
来源：arXiv：cs.LG · arxiv.org