# Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks

arXiv: https://arxiv.org/abs/2606.20583 · HTML: https://mlsanity.com/paper/2606.20583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 7 of 7)

- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing](https://mlsanity.com/paper/2405.10521/) (2024)
- [Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks](https://mlsanity.com/paper/2407.10147/) (2024)
- [JEPA-MSAC: A Joint-Embedding Predictive Architecture for Multimodal Sensing-Assisted Communications](https://mlsanity.com/paper/2603.29796/) (2026)
- [Interference Mitigation for Network-Level ISAC: An Optimization Perspective](https://mlsanity.com/paper/2402.09974/) (2024)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)

## Abstract (arXiv)

Conventional wireless networks rely on instantaneous channel state information (CSI) and react to channel variations without explicitly modeling the physical environment, limiting their ability to handle blockage, mobility, and interference in dynamic deployments. Paradigms such as Integrated Sensing and Communication (ISAC) add sensing capabilities but lack explicit environment modeling and decision-making. In this article, we propose Physical-AI: a new architecture for environment-aware wireless networking, where radio signals enable sensing, modeling, and interaction with the environment in addition to data transmission. The framework proposes a self-supervised spatiotemporal radio foundation model for transforming distributed radio observations into a shared latent environmental representation. Multiple inference heads operate on this representation to estimate key environmental properties, including blockage, user distribution, mobility dynamics, and interference structure. A task-specific neural decision layer maps this representation to proactive, context-aware control actions. By integrating perception, world modeling, and decision-making in a closed loop, the proposed framework goes beyond ISAC and establishes Physical-AI as a promising architecture for intelligent 6G systems. Simulation results show that the proposed predictive framework reduces outage probability and blockage-response latency, particularly under increasing beam-switching delays.