Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communication system in terms of capacity, spectral, and energy efficiency. The performance of MIMO systems is highly dependent on the quality of channel state information (CSI). Predicting CSI is, therefore, essential for improving communication system performance, particularly in MIMO systems, since it represents key characteristics of a wireless channel, including propagation, fading, scattering, and path loss. This study proposes a foundation model inspired by BERT, called BERT4MIMO, which is specifically designed to process high-dimensional CSI data from massive MIMO systems. BERT4MIMO offers superior performance in reconstructing CSI under varying mobility scenarios and channel conditions through deep learning and attention mechanisms. The experimental results demonstrate the effectiveness of BERT4MIMO in a variety of wireless environments.
A Graph Foundation Model for Large-Scale MIMO Detection
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；Department of Electrical and Computer Engineering, University of California, Santa Cruz（加州大学圣克鲁兹分校电气与计算机工程系）；Institute of Communications Engineering, National Sun Yat-sen University（国立中山大学通信工程研究所）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）
Massive MIMO-OFDM Channel Acquisition with Multi-group Adjustable Phase Shift Pilots
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信实验室）；Purple Mountain Laboratories（紫金山实验室）；University of Delaware（特拉华大学）
Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
School of Integrated Technology, Yonsei University（延世大学整合技术学院）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China（南京东南大学国家移动通信研究实验室）；Purple Mountain Laboratories, Nanjing 211100, China（南京紫金山实验室）；Huawei Technologies Co., Ltd., Suzhou 215100, China（华为技术有限公司）；School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, 2052, Australia（新南威尔士大学电子与电信学院）；Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 USA（德克萨斯大学达勒姆分校电子与计算机工程系）
Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）；Department of Electrical and Computer Engineering, University of Delaware（德克萨斯大学电气与计算机工程系）
