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…
Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization
Zirui Chen, Zhaoyang Zhang, Hongning Ruan +8
Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily on the scenario-dependent absolu…
Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model
Zirui Chen, Zhaoyang Zhang, Chenyu Liu +1
Research on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks is emerging. However, breakthroughs in generaliza…
VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems
Ziqing Xing, Zhaoyang Zhang, Zirui Chen +3
Recently, studies have shown the potential of integrating field-type iterative methods with deep learning (DL) techniques in solving inverse scattering problems (ISPs). In this art…