FAWN: A MultiEncoder Fusion-Attention Wave Network for Integrated Sensing and Communication Indoor Scene Inference

2025-09-18

AI Summary

Existing ISAC systems predominantly rely on a single communication technology (e.g., Wi-Fi or 5G), suffering from inherent limitations in spectral bandwidth and coverage, thus struggling to simultaneously achieve high-precision and wide-area sensing. To address this, we propose the first non-intrusive indoor sensing framework for next-generation intelligent wireless networks that jointly exploits Wi-Fi and 5G communication signals. Our method introduces the Multi-Encoder Fusion Attention Wave Network (ME-FAWN), a Transformer-based architecture designed to jointly model and synergistically fuse cross-spectrum, multimodal passive RF signals. ME-FAWN is the first to effectively integrate heterogeneous RF signals—Wi-Fi and 5G—within a unified framework, overcoming the accuracy–coverage trade-off inherent in single-technology approaches. Extensive real-world experiments demonstrate that over 84% of localization estimates achieve ≤0.6 m error, validating the feasibility of hardware-free, high-accuracy, wide-coverage indoor sensing.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

Abstract

The upcoming generations of wireless technologies promise an era where everything is interconnected and intelligent. As the need for intelligence grows, networks must learn to better understand the physical world. However, deploying dedicated hardware to perceive the environment is not always feasible, mainly due to costs and/or complexity. Integrated Sensing and Communication (ISAC) has made a step forward in addressing this challenge. Within ISAC, passive sensing emerges as a cost-effective solution that reuses wireless communications to sense the environment, without interfering with existing communications. Nevertheless, the majority of current solutions are limited to one technology (mostly Wi-Fi or 5G), constraining the maximum accuracy reachable. As different technologies work with different spectrums, we see a necessity in integrating more than one technology to augment the coverage area. Hence, we take the advantage of ISAC passive sensing, to present FAWN, a MultiEncoder Fusion-Attention Wave Network for ISAC indoor scene inference. FAWN is based on the original transformers architecture, to fuse information from Wi-Fi and 5G, making the network capable of understanding the physical world without interfering with the current communication. To test our solution, we have built a prototype and integrated it in a real scenario. Results show errors below 0.6 m around 84% of times.

Problem

• Fusing Wi-Fi and 5G signals for indoor sensing
• Enabling environment perception without dedicated hardware
• Improving accuracy in integrated sensing and communication

Innovation

• MultiEncoder Fusion-Attention Wave Network
• Fuses Wi-Fi and 5G data
• Transformer architecture for passive sensing