5 papers
Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction
Zirui Chen, Ziqing Xing, Zhaoyang Zhang +5
Data representation is a fundamental issue in deep learning. However, as wireless data scales and deeply couples across many physical domains such as time, space, and frequency, ex…
Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt
Hongning Ruan, Zhaoyang Zhang, Zirui Chen +2
Channel state information (CSI) is critical for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Pilot-based channel estimation methods suf…
Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design
Ziqing Xing, Zhaoyang Zhang, Zirui Chen +3
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 statio…
Implicit Neural Compression of Point Clouds
Hongning Ruan, Yulin Shao, Qianqian Yang +3
Point clouds have gained prominence across numerous applications due to their ability to accurately represent 3D objects and scenes. However, efficiently compressing unstructured,…
Point Cloud Compression with Implicit Neural Representations: A Unified Framework
Hongning Ruan, Yulin Shao, Qianqian Yang +2
Point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes. Nevertheless, effectively compressing…