5 papers
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…
Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
Jun Jiang, Yuan Gao, Xinyi Wu +1
Artificial intelligence (AI) has emerged as a pivotal enabler for next-generation wireless communication systems. However, conventional AI-based models encounter several limitation…
AI/ML for mobile networks: Current status in Rel. 19 and challenges ahead
Yuan Gao, Xinyi Wu, Jun Jiang +6
The transformative power of artificial intelligence (AI) and machine learning (ML) is recognized as a key enabler for sixth generation (6G) mobile networks by both academia and ind…
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…