7 citations · 8 across the 7 of their papers we have counts for
12 papers
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
Zehua Jiang, Fenghao Zhu, Xinquan Wang +2
Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster sampling compared to diffusion models…
One-Step Generative Channel Estimation via Average Velocity Field
Zehua Jiang, Fenghao Zhu, Siming Jiang +5
Generative models have shown immense potential for wireless communication by learning complex channel data distributions. However, the iterative denoising process associated with t…
Recursive Flow: A Generative Framework for MIMO Channel Estimation
Zehua Jiang, Fenghao Zhu, Chongwen Huang +5
Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In…
Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations
Bohao Wang, Zitao Shuai, Fenghao Zhu +6
Multimodal fingerprinting is a crucial technique to sub-meter 6G integrated sensing and communications (ISAC) localization, but two hurdles block deployment: (i) the contribution e…
Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
Xinquan Wang, Fenghao Zhu, Zhaohui Yang +5
Large artificial intelligence (AI) models offer revolutionary potential for future wireless systems, promising unprecedented capabilities in network optimization and performance. H…
TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models
Xinquan Wang, Fenghao Zhu, Chongwen Huang +5
Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Telecom) due to technical complexity, specialized terminology, and rapidly e…