Flow matching-based generative models for MIMO channel estimation
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；ZGC Institute of Ubiquitous-X Innovation and Applications（ZGC 全息-X创新与应用研究院）
Wenkai Liu, Nan Ma, Jianqiao Chen, Xiaoxuan Qi, Yuhang Ma
Diffusion model (DM)-based channel estimation, which generates channel samples via a posteriori sampling stepwise with denoising process, has shown potential in high-precision channel state information (CSI) acquisition. However, slow sampling speed is an essential challenge for recent developed DM-based schemes. To alleviate this problem, we propose a novel flow matching (FM)-based generative model for multiple-input multiple-output (MIMO) channel estimation. We first formulate the channel estimation problem within FM framework, where the conditional probability path is constructed from the noisy channel distribution to the true channel distribution. In this case, the path evolves along the straight-line trajectory at a constant speed. Then, guided by this, we derive the velocity field that depends solely on the noise statistics to guide generative models training. Furthermore, during the sampling phase, we utilize the trained velocity field as prior information for channel estimation, which allows for quick and reliable noise channel enhancement via ordinary differential equation (ODE) Euler solver. Finally, numerical results demonstrate that the proposed FM-based channel estimation scheme can significantly reduce the sampling overhead compared to other popular DM-based schemes, such as the score matching (SM)-based scheme. Meanwhile, it achieves superior channel estimation accuracy under different channel conditions.
Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications
Beijing University of Posts and Telecommunications（北京邮电大学）；ZGC Institute of Ubiquitous-X Innovation and Applications（中关村泛在X创新与应用研究院）；Shanghai Jiao Tong University（上海交通大学）
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）
Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China（国家移动通信研究中心，东南大学，南京）；Purple Mountain Laboratories, Nanjing 211100, China（紫金山实验室，南京）；School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, 2052, Australia（电气工程与电信学院，新南威尔士大学，悉尼）；Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA（电气与计算机工程系，德雷克塞尔大学，纽卡斯尔）
CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels
School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
Generative Modeling of Quantum Distribution with Functional Flow Matching
Graduate School of Data Science, Seoul National University, Seoul, Korea（首尔国立大学数据科学研究生院）；Department of Statistics and Data Science, Yonsei University, Seoul, Korea（延世大学统计与数据科学系）；Department of Applied Statistics, Yonsei University, Seoul, Korea（延世大学应用统计系）；Google Research, Mountain View, California, United States（谷歌研究）
Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission
School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology（电气工程与计算机科学系（EECS），皇家理工学院）；College of Information and Communication Engineering, Sungkyunkwan University（信息与通信工程学院，成均馆大学）
University of Toronto（多伦多大学）；Canadian Institute for Advanced Research（加拿大高等研究院）
我们考虑从数据中学习深度生成模型的问题。我们制定了一种方法，该方法通过多层感知器的单次前馈来生成独立样本，如最近提出的生成对抗网络（Goodfellow et al.，2014年）。然而，训练一个生成对抗网络需要仔细优化一个困难的极小极大程序。相反，我们利用了一种被称为最大平均差异（MMD）的统计假设检验技术，这导致了一个简单的目标，可以被解释为匹配数据集和模型样本之间的所有统计阶数，并且可以通过反向传播进行训练。我们通过将我们的生成网络与自动编码器网络相结合，使用MMD来学习生成代码，然后可以解码生成样本，从而进一步提高了这种方法的性能。我们表明，这些技术的组合产生优秀的生成模型相比，MNIST和多伦多人脸数据库上测量的基线方法。
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（哈里·米尔顿·斯图尔特工业与系统工程学院，佐治亚理工学院）；Department of Mathematics, Duke University（杜克大学数学系）
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach
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）；State Key Laboratory of CNS/ATM, Beijing Institute of Technology（北京理工大学 CNS/ATM 国家重点实验室）；Beijing Institute of Technology（北京理工大学）；MIT Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology（MIT 复杂场智能感知实验室，北京理工大学）；Advanced Technology Research Institute, Beijing Institute of Technology（北京理工大学先进技术研究院）；Yangtze Delta Region Academy, Beijing Institute of Technology（长江三角洲地区研究院，北京理工大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；Department of Electrical and Computer Engineering, University of Delaware（德雷塞尔大学电子与计算机工程系）
Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling
Wyoming Seminary（怀俄明神学院）；Middle Tennessee State University（中田纳西州立大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Tsinghua University（清华大学）；Simons Institute for the Theory of Computing, University of California, Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Anhui University（安徽大学）
Soft Graph Diffusion Transformer for MIMO Detection
College of Information Science and Electronic Engineering（信息科学与电子工程学院）；Theory Lab, Central Research Institute, 2012 Labs（理论实验室、中央研究所、2012实验室）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions
State Key Laboratory of Advanced Rail Autonomous Operation（先进轨道交通自主运行状态关键实验室）；Beijing Jiaotong University（北京交通大学）；School of Electronics and Information Engineering（电子与信息工程学院）；University of Hong Kong（香港大学）；College of Computing & Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）；Hong Kong University of Science and Technology（香港科技大学）
