Wireless Energy Transfer (WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze Channel State Information (CSI)-free multi-antenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AA-SS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be $π$ phase-shifted for optimum average energy performance under AA-SS. Our numerical results evidenced that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or number of antennas is not always beneficial under such scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS and/or large number of antennas.
6D Movable Antenna for Internet of Vehicles: CSI-Free Dynamic Antenna Configuration
Jiangnan University（江南大学）；Nanchang University（南昌大学）；Tsinghua University（清华大学）；Brunel University（布鲁内尔大学）；Shanghai Jiao Tong University（上海交通大学）；the Hong Kong University of Science and Technology（香港科技大学）
Shift-MoE-Based DJSCC for CSI Feedback in Multi-User Pinching-Antenna Systems
Shenzhen University（深圳大学）；Shenzhen Technology University（深圳技术大学）；Pengcheng Laboratory（鹏城实验室）；Hunan Institute of Science and Technology（湖南理工学院）；Hunan University（湖南大学）
Movable-Antenna Enabled Robust Vehicular Consumer Networks Under Imperfect CSI
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院电气电子工程系）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与技术工程学院）
Technische Universität (TU) Wien（维也纳工业大学）；Christian Doppler Laboratory for Dependable Wireless Connectivity for the Society in Motion（克里斯蒂安·多普勒移动社会可靠无线连接实验室）
CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Department of Electrical and Electronic Engineering（电子与电气工程系）；Department of Information Science and Engineering（信息科学与工程系）；KTH Royal Institute of Technology（皇家理工学院）
WiFo-INR: A Wireless Foundation Model Based on Implicit Neural Representations
State Key Laboratory of Photonics and Communications, School of Electronics, Peking University（光电通信国家重点实验室，电子学院，北京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks
研究了利用大规模多输入多输出（MaMIMO）系统的信道状态信息（CSI）进行用户定位的性能。为了从CSI中推断用户的位置，设计了一个卷积神经网络，并通过一个新的数据集进行评估。该数据集包含使用三种不同天线拓扑的室内MaMIMO CSI测量，覆盖2.5 m x 2.5 m的室内区域。我们表明，我们可以训练一个卷积神经网络（CNN）模型来估计该区域内的用户的位置，平均误差小于半个波长。此外，一旦模型在给定的场景和天线拓扑结构上进行了训练，迁移学习就用于将所获得的知识重新用于具有显著不同的天线拓扑结构和配置的另一场景。我们的研究结果表明，它是可能的进一步训练的CNN只使用少量的额外标记的样本的新的拓扑结构。这种迁移学习方法能够达到准确的结果，为CNN支持的基于CSI的实用定位系统铺平了道路。
Rotatable Antenna-Enhanced Secure Integrated Sensing and Communications Under Imperfect CSI
Beihang University（北京航空航天大学）；State Key Laboratory of CNS/ATM（空管通信导航国家重点实验室）；Queen’s University Belfast（贝尔法斯特女王大学）；State Key Laboratory of Environment Characteristics and Effects for Near-Space（临近空间环境特性与效应国家重点实验室）；Beijing Institute of Technology（北京理工大学）
6D Movable Antenna Enhanced Cell-free MIMO: Two-timescale Decentralized Beamforming and Antenna Movement Optimization
University of Electronic Science and Technology of China（电子科技大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）；Kash Institute of Electronics and Information Industry（喀什电子信息产业研究院）
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）
