ATIS Introduces Wireless Physical Layer Foundation Models for Next-Generation Wireless Intelligence
WASHINGTON, Nov. 2, 2026 — ATIS today announced the release of Wireless Physical Layer Foundation Models for Advanced Communication, Sensing, and Neuro-Symbolic Decision-Making, a new white paper exploring how artificial intelligence (AI) could help future wireless networks better understand changing radio conditions, adapt their operations, and make more informed decisions.
The paper introduces Wireless Physical Layer Foundation Models (WPFMs), an emerging approach that uses AI to learn from large amounts of wireless signal data to give networks a shared understanding they can reuse. Rather than building a separate AI model for each network function, a WPFM establishes one common understanding of the wireless environment that supports many applications at once.
This shared understanding could help networks predict changes in radio conditions, improve communications performance, and support new wireless sensing. It also reduces the need for individual network functions to collect and process similar data or train separate AI models, improving efficiency and coordination across wireless systems.
The white paper also examines how WPFMs can be combined with neuro-symbolic AI, which brings together AI’s ability to recognize patterns with rule-based reasoning. This combination could help wireless networks make decisions that are not only more adaptive but also easier to explain and evaluate against established policies and operational requirements.
“Future wireless networks will need to do more than respond to changing conditions. They will need to understand their environments and make increasingly complex decisions,” said ATIS President and CEO Susan Miller. “This research explores how a shared foundation for AI could help wireless networks operate more efficiently, adapt more effectively, and make decisions that can be better understood and trusted.”
“Learning from large amounts of unlabeled wireless signal data means Wireless Physical Layer Foundation Models need less labelled data for each new task,” said Lead Researcher for the WPFM Jaron Fontaine, imec – IDLab Ghent – Ghent University. “Combined with neuro-symbolic reasoning, they can help networks reason about changing conditions and make decisions that are easier to explain.”
The white paper highlights the potential for WPFMs to support more integrated and intelligent wireless systems that can move beyond individual AI capabilities as the industry advances toward 6G. It also identifies opportunities for further research and industry collaboration.
Access Wireless Physical Layer Foundation Models Foundation Models for Advanced Communication, Sensing, and Neuro-Symbolic Decision-Making. ATIS welcomes engagement from network operators, technology suppliers, and researchers across the wireless community. Contact ATIS Membership Director Rich Moran.
