13 citations · 13 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
AI/ML for Mobile Networks: Release 19 Status and Future Challenges
Yuan Gao, Xinyi Wu, Jun Jiang +8
Artificial intelligence (AI) and machine learning (ML) have become essential drivers of sixth-generation (6G) mobile networks. Unlike earlier surveys that mainly review high-level…
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…
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…
Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
Jun Jiang, Yuan Gao, Xinyi Wu +1
Artificial intelligence (AI) models for wireless systems are commonly optimized for fixed tasks, channel distributions, and system configurations, which limits their reuse when dow…