# Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

arXiv: https://arxiv.org/abs/2404.14140 · HTML: https://mlsanity.com/paper/2404.14140/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 20 of 20)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022)
- [Denoising Diffusion Models for Plug-and-Play Image Restoration](https://mlsanity.com/paper/2305.08995/) (2023)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [Practical Deep Raw Image Denoising on Mobile Devices](https://mlsanity.com/paper/2010.06935/) (2020)
- [SinDDM: A Single Image Denoising Diffusion Model](https://mlsanity.com/paper/2211.16582/) (2022)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations](https://mlsanity.com/paper/2304.11267/) (2023)
- [AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing](https://mlsanity.com/paper/2303.01896/) (2023)
- [mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking](https://mlsanity.com/paper/1812.03103/) (2018)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach](https://mlsanity.com/paper/2211.12783/) (2022)
- [A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks](https://mlsanity.com/paper/2309.01426/) (2023)
- [Positioning Using Visible Light Communications: A Perspective Arcs Approach](https://mlsanity.com/paper/2204.08131/) (2022)
- [Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing](https://mlsanity.com/paper/2401.00160/) (2023)

## Built on by (most cited first, 10 of 10)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?](https://mlsanity.com/paper/2412.03959/) (2024)
- [Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey](https://mlsanity.com/paper/2504.07635/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [Channel-Aware Conditional Diffusion Model for Secure MU-MISO Communications](https://mlsanity.com/paper/2602.03524/) (2026)
- [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024)
- [Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design](https://mlsanity.com/paper/2505.12664/) (2025)
- [Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing](https://mlsanity.com/paper/2604.16558/) (2026)
- [Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling](https://mlsanity.com/paper/2609.15379/) (2026)

## Abstract (arXiv)

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also in signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human-induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling the detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR's time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD's accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.