Extremely large-scale antenna arrays, tremendously high frequencies, and new types of antennas are three clear trends in multi-antenna technology for supporting the sixth-generation (6G) networks. To properly account for the new characteristics introduced by these three trends in communication system design, the near-field spherical-wave propagation model needs to be used, which differs from the classical far-field planar-wave one. As such, near-field communication (NFC) will become essential in 6G networks. In this tutorial, we cover three key aspects of NFC. 1) Channel Modelling: We commence by reviewing near-field spherical-wave-based channel models for spatially-discrete (SPD) antennas. Then, uniform spherical wave (USW) and non-uniform spherical wave (NUSW) models are discussed. Subsequently, we introduce a general near-field channel model for SPD antennas and a Green's function-based channel model for continuous-aperture (CAP) antennas. 2) Beamfocusing and Antenna Architectures: We highlight the properties of near-field beamfocusing and discuss NFC antenna architectures for both SPD and CAP antennas. Moreover, the basic principles of near-field beam training are introduced. 3) Performance Analysis: Finally, we provide a comprehensive performance analysis framework for NFC. For near-field line-of-sight channels, the received signal-to-noise ratio and power-scaling law are derived. For statistical near-field multipath channels, a general analytical framework is proposed, based on which analytical expressions for the outage probability, ergodic channel capacity, and ergodic mutual information are obtained. Finally, for each aspect, topics for future research are discussed.
第三代合作伙伴计划（3GPP）最近发布了其版本16，其中包括基于5G新无线电（NR）空中接口的第一个车辆到一切（V2X）标准。5G NR V2X在5G NR空中接口之上引入了高级功能，以支持具有严格要求的连接和自动驾驶用例。本文介绍了用于V2X通信的3GPP Release 16 5G NR V2X标准的深入教程，特别关注侧链路，因为它是5G NR V2X最重要的部分。本文的主要部分是深入讨论5G NR V2X的关键方面：物理层，资源分配，服务质量管理，Uu接口的增强和V2N（车辆到网络）通信的移动性管理，以及5G NR V2X和LTE V2X之间的共存机制。我们还回顾了用例、系统架构，并描述了5G NR V2X的评估方法和模拟假设。最后，我们展望了可能的5G NR V2X增强功能，包括第17版中确定的增强功能。
End-to-End Deep Learning in Wireless Communication Systems: A Tutorial Review
University of Arkansas at Little Rock（阿肯色大学小石城分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；African Leadership Academy（非洲领导力学院）；Cornell University（康奈尔大学）；University of Southampton（南安普顿大学）；École de technologie supérieure (ETS), Université du Québec（魁北克理工大学）
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Beijing University of Posts and Telecommunications（北京邮电大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Princeton University（普林斯顿大学）；The University of Edinburgh（爱丁堡大学）；Virginia Tech（弗吉尼亚理工大学）；Huawei France R & D（华为法国研发中心）
