Physics-Informed Implicit Neural Representation for Wireless Imaging in RIS-Aided ISAC System

2026-01-21

AI Summary

This work proposes a reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) imaging framework that overcomes the limitations of conventional wireless imaging methods, which suffer from poor multipath separation accuracy and a scarcity of high-quality datasets. By uniquely integrating implicit neural representations (INRs) with physics-informed loss functions, the approach leverages positional encoding and sinusoidal activation networks to model the continuous geometry and scattering characteristics of targets. This paradigm eliminates the reliance on explicit multipath processing and large-scale training data, enabling high-fidelity, resolution-independent imaging with strong generalization capabilities. Moreover, the framework reveals an intrinsic focal-length property inherent to the system. Extensive simulations demonstrate that the proposed method significantly outperforms existing state-of-the-art techniques in imaging performance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager

Problem

• wireless imaging
• multipath separation
• dataset acquisition
• RIS-aided ISAC
• physics-informed

Innovation

• Implicit Neural Representation
• Physics-Informed Learning
• Reconfigurable Intelligent Surface
• Integrated Sensing and Communication
• Wireless Imaging