most citedWiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models

22 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2026

Diffusion Models for Radio Map Estimation: Theoretical Performance Analysis and Sampling Rate Guideline

Zhiyuan Liu, Qingyu Liu, Shuhang Zhang +2

Radio maps, which characterize the spatial distribution of radio frequency metrics, such as the received signal strength, are essential for a wide range of wireless applications. T…

eess.SP2026

RIS-Aided Wireless Amodal Sensing for Single-View 3D Reconstruction

Yuhan Wang, Haobo Zhang, Qingyu Liu +2

Amodal sensing is critical for various real-world sensing applications because it can recover the complete shapes of partially occluded objects in complex environments. Among vario…

eess.SP2025

A Fine-Grained 3D Radio Map Construction Paradigm with Ultra-Low Sampling Rates by Large Generative Models

Zhiyuan Liu, Qingyu Liu, Shuhang Zhang +2

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained thr…

cs.ET2025

End-Edge Model Collaboration: Bandwidth Allocation for Data Upload and Model Transmission

Dailin Yang, Shuhang Zhang, Hongliang Zhang +1

The widespread adoption of large artificial intelligence (AI) models has enabled numerous applications of the Internet of Things (IoT). However, large AI models require substantial…

eess.SP2025★ 22 cited

WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models

Zhiyuan Liu, Shuhang Zhang, Qingyu Liu +2

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to impr…