In this work, we propose a novel data-driven machine learning (ML) technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations of high-dimensional channel state information (CSI), we incorporate a predictive component to capture the dynamics of the wireless system. Hence, we jointly learn a channel encoder that maps the estimated CSI to an appropriate latent space, and a predictor that models the relationships between such representations. Accordingly, our problem boils down to training a joint-embedding predictive architecture (JEPA) that simulates the latent dynamics of a wireless network from CSI. We present numerical evaluations on measured data and show that the proposed JEPA displays a two-fold increase in accuracy over benchmarks, for longer look-ahead prediction tasks.
Structured Latent Dynamics in Wireless CSI via Homomorphic World Models
Engineering Division, New York University (NYU), Abu Dhabi, UAE.（纽约大学阿布扎克分校工程系）；Center for Wireless Communications, University of Oulu, Finland.（奥卢大学无线通信中心）；NYU WIRELESS, NYU Tandon School of Engineering, New York, USA.（纽约大学无线技术实验室，纽约大学坦顿工程学院）
Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
College of Computer and Information Science（计算机与信息科学学院）；Southwest University（西南大学）；School of Engineering（工程学院）；University of Western Australia（西澳大学）；College of Food Science（食品科学学院）；Chongqing True Flavor Food Co LTD（重庆真味食品有限公司）
A Big Data Enabled Channel Model for 5G Wireless Communication Systems
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Beihang University（北京航空航天大学）；Shandong University（山东大学）；ShanghaiTech University（上海科技大学）；Shanghai Jiaotong University（上海交通大学）；Aalto University（阿尔托大学）；Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences (CAS)（中国科学院上海微系统与信息技术研究所）
Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks
Duzce University（杜兹切大学）；University of Luxembourg（卢森堡大学）；Carleton University（卡尔顿大学）；United States Army Research Laboratory（美国陆军研究实验室）；University of Hertfordshire（赫特福德大学）
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning
Center for Pervasive Communications and Computing, University of California, Irvine（普及通信与计算中心，加州大学伊维特分校）；Nokia Standards（诺基亚标准）；Universitat Pompeu Fabra（庞培法布拉大学）
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（华中科技大学电气与电子工程学院）；Cornell University（康奈尔大学）；Lassonde School of Engineering, York University（约克大学拉桑德工程学院）
