activity
20232025
most citedChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP2025

Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness

Yichen Cai, Jianhua Zhang, Li Yu +5

To meet the robust and high-speed communication requirements of the sixth-generation (6G) mobile communication system in complex scenarios, sensing- and artificial intelligence (AI…

eess.SP2025

Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek

Yizhu Zhao, Li Yu, Lianzheng Shi +2

Beam prediction is an effective approach to reduce training overhead in massive multiple-input multiple-output (MIMO) systems. However, existing beam prediction models still exhibi…

eess.SP20242 cited

ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence

Li Yu, Lianzheng Shi, Jianhua Zhang +4

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network desig…

eess.SP20241 cited

Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example

Lianzheng Shi, Jianhua Zhang, Li Yu +4

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system…

eess.SP2023

Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool

Jialin Wang, Jianhua Zhang, Yuxiang Zhang +6

The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well know…