109 citations · 252 across the 14 of their papers we have counts for
16 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…
Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches
Fenghao Zhu, Xinquan Wang, Chen Zhu +6
Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algo…
Liquid Neural Networks: Next-Generation AI for Telecom from First Principles
Fenghao Zhu, Xinquan Wang, Chen Zhu +1
Artificial intelligence (AI) has emerged as a transformative technology with immense potential to reshape the next-generation of wireless networks. By leveraging advanced algorithm…
Wireless large AI model: shaping the AI-empowered future of 6G and beyond
Fenghao Zhu, Xinquan Wang, Siming Jiang +22
The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligenc…
Beamforming Inferring by Conditional WGAN-GP for Holographic Antenna Arrays
Fenghao Zhu, Xinquan Wang, Chongwen Huang +5
The beamforming technology with large holographic antenna arrays is one of the key enablers for the next generation of wireless systems, which can significantly improve the spectra…
Robust Beamforming with Gradient-based Liquid Neural Network
Xinquan Wang, Fenghao Zhu, Chongwen Huang +5
Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication with the advanced beamforming technologies is a key enabler to meet the growing demands of future mobil…
Robust Continuous-Time Beam Tracking with Liquid Neural Network
Fenghao Zhu, Xinquan Wang, Chongwen Huang +6
Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum…
Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning
Fenghao Zhu, Xinquan Wang, Chongwen Huang +6
Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable w…
Multi-Sources Information Fusion Learning for Multi-Points NLOS Localization
Bohao Wang, Fenghao Zhu, Mengbing Liu +5
Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from chan…
Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning
Xinquan Wang, Fenghao Zhu, Qianyun Zhou +6
Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communicat…
Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
Fenghao Zhu, Bohao Wang, Zhaohui Yang +5
Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G. Thus, various techniques are lev…