# Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design

arXiv: https://arxiv.org/abs/2505.12664 · HTML: https://mlsanity.com/paper/2505.12664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 25 of 25)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models](https://mlsanity.com/paper/1810.01367/) (2018)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective](https://mlsanity.com/paper/2310.01036/) (2023)
- [Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments](https://mlsanity.com/paper/2404.14140/) (2024)
- [Over-the-Air Multi-View Pooling for Distributed Sensing](https://mlsanity.com/paper/2302.09771/) (2023)
- [Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments](https://mlsanity.com/paper/2307.04111/) (2023)
- [Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model](https://mlsanity.com/paper/2412.09041/) (2024)
- [Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer](https://mlsanity.com/paper/2401.03420/) (2024)
- [Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate](https://mlsanity.com/paper/2403.19409/) (2024)
- [Environment Sensing Considering the Occlusion Effect: A Multi-View Approach](https://mlsanity.com/paper/2207.00776/) (2022)
- [Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization](https://mlsanity.com/paper/2504.08811/) (2025)
- [Multibeam for Joint Communication and Sensing Using Steerable Analog Antenna Arrays](https://mlsanity.com/paper/1810.04105/) (2018)
- [Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication](https://mlsanity.com/paper/2312.16428/) (2023)
- [Deep Learning-based Design of Uplink Integrated Sensing and Communication](https://mlsanity.com/paper/2403.01480/) (2024)
- [Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC](https://mlsanity.com/paper/2404.00582/) (2024)
- [Electromagnetic Property Sensing Based on Diffusion Model in ISAC System](https://mlsanity.com/paper/2407.03075/) (2024)
- [Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis](https://mlsanity.com/paper/2405.06364/) (2024)
- [VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems](https://mlsanity.com/paper/2405.18731/) (2024)
- [Electromagnetic Property Sensing and Channel Reconstruction Based on Diffusion Schr"odinger Bridge in ISAC](https://mlsanity.com/paper/2409.11651/) (2024)

## Abstract (arXiv)

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)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target's point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target's shape and EM properties.