In this work, we analyze the secrecy outage performance of a dual-hop relay system composed of multiple-input-multiple-output radio-frequency (RF) links and a free-space optical (FSO) link while a multiple-antenna eavesdropper wiretaps the confidential information by decoding the received signals from the resource node. The channel state information (CSI) of the RF and FSO links is considered to be outdated. We propose three transmit antenna selection (TAS) schemes to enhance the secrecy performance of the considered systems. The secrecy outage performance with different TAS schemes is analyzed and the effects of misalignment and detection technology on the secrecy outage performance of mixed systems are studied. We derive the closed-form expressions for probability density function (PDF) and cumulative distribution function (CDF) over Málaga channel with imperfect CSI. Then the closed-form expressions for the CDF and PDF of the equivalent signal-to-noise ratio (SNR) at the legitimate receiver over Nakagami-$m$ and Málaga channels are derived. Furthermore, the lower bound of the secrecy outage probability (SOP) with different TAS schemes are derived. Besides, the asymptotic results for SOP are investigated by exploiting the unfolding of Meijer's $G$-function when the electrical SNR of FSO link approaches infinity. Finally, Monte-Carlo simulation results are presented to testify the correctness of the proposed analysis. The results illustrate that the outdated CSI shows a strong effect on the secrecy outage performance. In addition, increasing the number of antennas at the source cannot significantly enhance the secrecy performance of the considered systems.
Self-Supervised Pretraining for Transmission-Relevant MIMO CSI Representation
Pohang University of Science and Technology（浦项科技大学）；Institute of New Media and Communications, Seoul National University（首尔大学新媒体与通信研究所）；Department of Electrical and Computer Engineering, Seoul National University（首尔大学电气与计算机工程系）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）
Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training
School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT)（北京邮电大学人工智能学院）；Institute for Communication Systems, University of Surrey（萨里大学通信系统研究所）；Purple Mountain Laboratories（紫金山实验室）
Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation
School of Software Technology, Zhejiang University（浙江大学软件学院）；State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（浙江大学杭州高新技术产业开发区（滨江）区块链与数据安全国家重点实验室）
Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G
National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications（北京邮电大学移动网络技术国家工程研究中心）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China（中央民族大学教育部民族语言智能分析与安全治理重点实验室）；China Telecom Corporation Limited Gansu Branch（中国电信股份有限公司甘肃分公司）
Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems
College of Information Science and Technology, Donghua University（东华大学信息科学与技术学院）；Department of Information and Computer Science, Keio University（Keio大学信息与计算机科学系）；School of Computing and Information Technology, Great Bay University（Great Bay大学计算与信息科技学院）；Great Bay Institute for Advanced Study (GBIAS)（Great Bay先进研究 institutes）；Department of Engineering, Nottingham Trent University（Nottingham Trent大学工程系）
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）；State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University（北京交通大学轨道交通控制与安全国家重点实验室）
CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning
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）；Huawei Technologies Co., Ltd.（华为技术有限公司）
