Triplet-Based Wireless Channel Charting: Architecture and Experiments
Huawei Technologies France（华为技术法国公司）
Paul Ferrand, Alexis Decurninge, Luis G. Ordoñez, Maxime Guillaud
备注：
Accepted for publication in IEEE JSAC Series on Machine Learning for Communications and Networks. A conference version was published in IEEE Globecom 2020
Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting the fundamental parameters governing its distribution. We introduce a novel channel charting approach based on triplets of samples. The proposed algorithm learns a meaningful similarity metric between CSI samples on the basis of proximity in their respective acquisition times, and simultaneously performs dimensionality reduction. We present an extensive experimental validation of the proposed approach on data obtained from a commercial Massive MIMO system; in particular, we evaluate to which extent the obtained channel chart is similar to the user location information, although it is not supervised by any geographical data. Finally, we propose and evaluate variations in the channel charting process, including the partially supervised case where some labels are available for part of the dataset.
Dynamic Channel Charting: An LSTM-AE-based Approach
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Department of Intelligent Science, Xi’an Jiaotong-Liverpool University（西交利物浦大学智能工程学院）；School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications（西安邮电大学通信与信息工程学院）
A Sheaf-Theoretic Framework for Distributed Multi-Site Channel Charting
Sapienza University of Rome（罗马智慧大学）；National Inter-University Consortium for Telecommunications (CNIT)（国家大学电信联盟）；Department of Information Engineering, Electronics, and Telecommunications, Sapienza University（罗马智慧大学信息与电信工程学院）
Generalized Pinching-Antenna Systems: A Tutorial on Principles, Design Strategies, and Future Directions
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Southwest Jiaotong University（西南交通大学）；Southeast University（东南大学）；Nanyang Technological University（南洋理工大学）；Friedrich-Alexander-University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希·阿列克桑德大学）；The University of British Columbia（不列颠哥伦比亚大学）；Memorial University（纪念大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Hong Kong University of Science and Technology（香港科技大学）；Yonsei University（延世大学）；MediaTek（联发科）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Imperial College（帝国理工学院）
