13 citations · 35 across the 24 of their papers we have counts for
12 papers · 1 filter
CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
Yuan Gao, Wenjun Yu, Jun Jiang +3
Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptation procedures, task definitions,…
Generalizable and Computational Efficient Channel Extrapolation for 6G: A Configurable AI-Driven Framework Built from a Modular Perspective
Yuan Gao, Xinyi Wu, Jiang Jun +5
Acquiring channel state information (CSI) with manageable overhead has been essential to provide high-performance communication services, which is extremely challenging in the emer…
6G Native AI and Channel Foundation Models
Shugong Xu, Jun Jiang, Yuan Gao
The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems. However, both the meaning of na…
CSI-CLIP++: A Scalable Channel Foundation Model for Wireless Communication via CIR-CSI Consistency
Jun Jiang, Wenjun Yu, Yunfan Li +2
Self-supervised learning can exploit large-scale unlabeled channel data to improve the transferability of wireless AI models. Existing channel foundation models are often built on…
Generalizable and Robust Beam Prediction for 6G Networks: An Deep-Learning Framework with Positioning Feature Fusion
Yanliang Jin, Yunfan Li, Jiang Jun +5
Beamforming (BF) is essential for enhancing system capacity in fifth generation (5G) and beyond wireless networks, yet exhaustive beam training in ultra-massive multiple-input mult…
Dynamic Channel Charting: An LSTM-AE-based Approach
Yuan Gao, Wenjing Xie, Yiming Liu +3
With the development of the sixth-generation (6G) communication system, Channel State Information (CSI) plays a crucial role in improving network performance. Traditional Channel C…
Near-Field Positioning for XL-MIMO Uniform Circular Arrays: An Attention-Enhanced Deep Learning Approach
Yuan Gao, Xinyu Guo, Han Li +2
In the evolving landscape of sixth-generation (6G) mobile communication, multiple-input multiple-output (MIMO) systems are incorporating an unprecedented number of antenna elements…
Channel Extrapolation for MIMO Systems with the Assistance of Multi-path Information Induced from Channel State Information
Yuan Gao, Xinyi Wu, Jiang Jun +5
Acquiring channel state information (CSI) through traditional methods, such as channel estimation, is increasingly challenging for the emerging sixth generation (6G) mobile network…
Efficient UAV trajectory prediction: A multi-modal deep diffusion framework
Yuan Gao, Xinyu Guo, Wenjing Xie +4
To meet the requirements for managing unauthorized UAVs in the low-altitude economy, a multi-modal UAV trajectory prediction method based on the fusion of LiDAR and millimeter-wave…
ReinPath: A Multimodal Reinforcement Learning Approach for Pathology
Kangcheng Zhou, Jun Jiang, Qing Zhang +3
Interpretability is significant in computational pathology, leading to the development of multimodal information integration from histopathological image and corresponding text dat…
CSI-MAE: A Masked Autoencoder-based Channel Foundation Model
Jun Jiang, Xiaolong Ruan, Shugong Xu
Self-Supervised Learning (SSL) has emerged as a key technique in machine learning, tackling challenges such as limited labeled data, high annotation costs, and variable wireless ch…
AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research
Yuan Gao, Zichen Lu, Xinyi Wu +7
CSI extrapolation is an effective method for acquiring channel state information (CSI), essential for optimizing performance of sixth-generation (6G) communication systems. Traditi…