8 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…
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…
Robust Hybrid Beamforming with Liquid Crystal Antennas and Liquid Neural Networks
Xinquan Wang, Mingjun Ying, Hongren Chen +6
Sub-terahertz (sub-THz) multi-user multiple-input multiple-output (MU-MIMO) systems unlock immense bandwidth for 6G wireless communications. However, practical deployment of wirele…
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…