Wireless Foundation Models

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UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing
Oct 7, 2026Daniel Eckhoff, Hua Kang, Zhitang Chen +1Wi-Fi Sensing Wireless Foundation Models
Huawei Noah’s Ark Lab
GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
Aug 10, 2026Chenghong Bian, Chaozheng Wen, Hongze Chen +1Wireless Foundation Models Wireless Localization
1Hong Kong University of Science and Technology
MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation
Aug 5, 2026Blessed Guda, Kayley Sze, Carlee Joe-WongWireless Foundation Models Wireless Localization
Carnegie Mellon University Pittsburgh, USA
Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds
Jul 31, 2026Chaozheng Wen, Chenghong Bian, Hongze Chen +1Wireless Foundation Models Wireless Communications
Hong Kong University of Science and Technology
WALoMA: A Multitask Wireless Foundation Model via Adaptive Low-Rank Masked Autoencoders
Jul 28, 2026Madi Makin, Asmaa Abdallah, Abdulkadir Celik +1Wireless Foundation Models Masked Autoencoders
Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST), Thuwal, KSA 23955-6900 · School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Jul 18, 2026Yangjing Wang, Ouya Wang, Shenglong Zhou +1Wireless Foundation Models Hierarchical Representation Learning
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2BX London, U.K. · School of Mathematics and Statistics, Beijing Jiaotong University, China
Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
Jul 17, 2026Yong Chu, Xun Zhou, Zenglin Xu +2Wireless Foundation Models Prompt Tuning
Harbin Institute of Technology, Shenzhen · Pengcheng Laboratory · Shanghai Academy of AI for Science +1
CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
Jul 16, 2026Yuan Gao, Wenjun Yu, Jun Jiang +3Wireless Foundation Models Benchmark Design
School of Communication and Information Engineering, Shanghai University, Shanghai, China · Xi’an Jiaotong-Liverpool University, Suzhou, China
The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments
Jun 30, 2026Jiayi Chen, Weiting Ou, Guangxu ZhuWireless Foundation Models Wi-Fi Sensing
Shenzhen Research Institute of Big Data · The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute (SLAI)
Fast Wireless Foundation Models with Early-Exits
Jun 28, 2026Omar Mashaal, Hatem Abou-ZeidWireless Foundation Models Efficient Inference
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G
Jun 12, 2026Chenyu Zhang, Xinchen Lyu, Chenshan Ren +2Wireless Foundation Models Representation Learning
National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China · China Telecom Corporation Limited Gansu Branch, Gansu 730000, China
APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations
Jun 10, 2026Swadhin Pradhan, Niloo Bahadori, Peiman AminiMultivariate Time Series Forecasting Wireless Foundation Models
Cisco Systems, USA
OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments
Jun 9, 2026Lei Chu, Yuning Zhang, Omer Gokalp Serbetci +3Wireless Foundation Models Domain Generalization
A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
Jun 9, 2026Yuxuan Shi, Tingting Yang, Kangning Ma +4Wireless Foundation Models Adaptive Model Routing
Department of Broadband Communication, Pengcheng Laboratory, Shenzhen, 518000, China
LatentWave: JEPA Pretraining for Wireless Foundation Models
Jun 4, 2026Ahmed Mohamed, Ahmed Aboulfotouh, Hatem Abou-ZeidWireless Foundation Models Self-Supervised Pre-Training
Department of Electrical and Software Engineering, University of Calgary, Canada
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
May 28, 2026Liwen Jing, Yisha Lu, Tingting Yang +5Wireless Foundation Models Wireless Communications
Pengcheng Laboratory Shenzhen, China
Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model
May 24, 2026Xindi Tong, Chee Wei Tan, H. Vincent PoorWireless Foundation Models Min-Max Optimization
College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore · Princeton University, United States
Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models
May 14, 2026Swadhin Pradhan, Shazal Irshad, Jerome HenryWireless Foundation Models Wireless Communications
Cisco Systems
How Big Should a Wireless Foundation Model Be?
May 8, 2026Wei-Lun Cheng, Wanjiun LiaoWireless Foundation Models Wireless Communications
Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
May 5, 2026Md Raihan Uddin, Tolunay Seyfi, Fatemeh AfghahWireless Foundation Models Automatic Modulation Classification
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models
May 1, 2026Chenyu Zhang, Xinchen Lyu, Chenshan Ren +4Rotary Positional Embeddings Wireless Foundation Models
National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Broadband Communication, Pengcheng Laboratory, Shenzhen 518055, China · Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China +1
AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G
Apr 19, 2026Kejia Bian, Meixia Tao, Jianhua Mo +2Wireless Foundation Models Representation Learning
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China · Shanghai Innovation Institute, Shanghai 200231, China

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing

Daniel Eckhoff, Hua Kang, Zhitang Chen, Jie Chuai

Huawei Noah’s Ark Lab

Wi-Fi Sensing Wireless Foundation Models Representation Learning

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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.

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