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[2505.12664] Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design
Summary
This paper presents a novel framework for high-precision target sensing using multi-view wireless channel state information (CSI) through a conditional generative learning approach.
Why It Matters
The integration of physical knowledge into machine learning models enhances the accuracy and flexibility of wireless sensing technologies. This research could significantly impact applications in telecommunications and remote sensing, providing insights into improving target reconstruction quality.
Key Takeaways
- Introduces a bipartite neural network architecture for target sensing.
- Utilizes multi-view CSI to enhance the reconstruction of target shapes and electromagnetic properties.
- Implements a conditional diffusion model with a weighted loss for improved performance.
- Demonstrates significant flexibility and performance improvements through extensive numerical results.
- Highlights the importance of physical correlations in enhancing machine learning models.
Electrical Engineering and Systems Science > Signal Processing arXiv:2505.12664 (eess) [Submitted on 19 May 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design Authors:Ziqing Xing, Zhaoyang Zhang, Zirui Chen, Hongning Ruan, Zhaohui Yang, Zhiyong Feng View a PDF of the paper titled Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design, by Ziqing Xing and 5 other authors View PDF Abstract:In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target's reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wa...
