视频集合Videos & Lectures

OpenISAC: 一个开源的通感一体化(ISAC)实时实验平台

面向6G通感一体化(ISAC)及低空无人机通感，东南大学及紫金山实验室曾勇教授团队推出OpenISAC，一个通用且高性能的开源平台，旨在弥合理论与实践之间的差距。 它完全基于开源软件 (UHD、C++、Python) 构建，支持基于 OFDM 的实时通信和感知。其核心特性是新颖的空口 (Over-the-Air) 感知同步机制，无需有线连接即可实现稳定的双站感知。 更多信息，请查阅我们的论文，项目主页，Github链接，以及QQ群： Z. Zhou, C. Zhang, X. Xu, and Y. Zeng, "OpenISAC: An Open-Source Real-Time Experimentation Platform for OFDM-ISAC with Over-the-Air Synchronization," submitted to IEEE Trans. Wireless Commun., Jan. 2026. [arXiv] 项目主页：https://openisac.zzw123app.top/index_zh.html GitHub链接：https://github.com/zhouzhiwen2000/OpenISAC QQ群：https://qm.qq.com/q/NIQRNGb0kY

低空无人机通信与感知2025

围绕低空经济及6G移动信息网络发展趋势，介绍了低空无人机通信与感知的相关理论与技术，包括：无人机通信与感知基础模型，无人机空地融合通信，无人机通信感知一体化，无人机集群通感一体化。

6G超大规模MIMO近场通信与感知

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a natural evolution of the contemporary massive MIMO technology, which further boosts the number of antennas by at least an order of magnitude, e.g., to several hundreds or even thousands of antennas. It will unprecedentedly improve the spectral efficiency and spatial resolution for wireless communication and sensing, and thus propelling the 6G usage scenarios such as immersive communication and massive communication into a reality. However, the evolution from massive MIMO to XL-MIMO is not simply an increase in the array size, but faces new theoretical and design challenges, in terms of near-field channel modeling, performance analysis, channel estimation, and practical implementation. This tutorial aims to provide an overview on near-field XL-MIMO communications and sensing. First, the basic near-field modeling for XL-MIMO is established, by considering the new characteristics of non-uniform spherical wave (NUSW) and spatial non-stationarity. Next, based on the near-field modeling, the performance analysis of XL-MIMO communication and sensing is presented, including the near-field signal-to-noise ratio (SNR) scaling laws, beam focusing pattern, achievable rate, spatial resolution, communication and sensing degrees-of-freedom (DoF). The new communication, localization and sensing capabilities enabled by near-field effect and sparse MIMO are highlighted.

信道知识地图(CKM)智能构建与应用

感谢《信息与智能学报（英文）》的邀请，做了《信道知识地图(CKM)智能构建与应用》的报告。 现有的无线通信与感知系统大多基于传统的环境未知范式，未能充分挖掘利用当地无线环境的先验信息，从而导致低效的环境感知与信道获取，难以满足未来信道大维化、节点密集化、硬件低成本化等发展需求。近年来提出的信道知识地图(Channel Knowledge Map, CKM)概念，通过融合区域内所有终端的海量历史数据，构建反映当地无线环境特征的信道知识库，从而直接根据终端位置或虚拟位置信息提前获取环境先验，赋能由传统环境未知迈向未来环境认知通信与感知的范式变革，为实现高效环境感知及信道获取提供了新思路。本报告将介绍CKM构建与应用的最新研究进展，通过讨论CKM的基本原理、基于CKM的通信与感知典型案例、基于传统及AI的CKM构建理论方法、以及初步实验验证等，试图回答关于CKM的五大关键问题(2W+3H)：什么是CKM, 为何需要CKM, 如何构建及利用CKM, 以及如何进行原型验证？

Rethinking Waveform for 6G: Delay-Doppler Alignment Modulation (DDAM)

非常感谢Sun Haijian老师的邀请，和University of Georgia的同学们分享了我们在6G波形方面的工作：时延多普勒对齐调制(DDAM）。欢迎大家批评指正，合作探讨。 Conventional multi-carrier waveforms based on time-frequency domain modulation face problems such as high peak-to-average power ratio (PAPR), serious out-of-band leakage, sensitivity to inter-carrier interference, and high cyclic prefix overhead. For future wireless systems, with the use of larger antenna arrays, higher frequency bands, and more powerful sensing capabilities, new opportunities arise for the design of wireless waveforms for 6G and beyond. To this end, this talk intends to explore the high spatial resolution of large antenna arrays and the multipath sparsity of high-frequency signals, and introduce the novel framework of Delay-Doppler alignment modulation (DDAM) based on joint processing in space-delay-Doppler domains. DDAM leverages path-based beamforming to distinguish multipath signals in the spatial domain, enabling delay and Doppler compensation and alignment. This will greatly reduce the delay Doppler spread of the channel and thus avoid the complicated channel equalization or large dimensional multi-carrier transmission. The talk will first introduce the background and motivation for proposing DDAM, and then introduce single-carrier and multi-carrier DDAM communications, followed by DDAM integrated sensing and communication (ISAC). Finally, preliminary experimental verification results are introduced.

UAVISAC4

低空无人机通信与感知-第三部分

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低空无人机通信与感知- Part2

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低空无人机通信与感知- Part1

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基于通信感知一体化的信道知识地图原型

本实验展示基于通信感知一体化（ISAC）的信道知识地图（CKM）原型系统，揭示ISAC与CKM之间的互惠共生关系。系统由一个ISAC基站（BS）、一个用户设备（UE）和一个服务器组成。通过在毫米波（mmWave）频段上使用正交频分复用（OFDM）波形，ISAC BS可以在与UE通信的同时感知环境并获取UE的位置。本原型系统将展示基于ISAC的CKM的构建和应用的完整过程。在CKM构建阶段，BS将UE反馈的信道信息存储在服务器上，并以UE的位置为索引建立数据库。在CKM应用阶段，BS根据UE的位置从CKM中查找最优波束索引，以实现免训练的毫米波波束对齐。

6G波形新思考：时延多普勒对齐调制

传统基于时频域调制的多载波无线传输波形面临着信号峰均比高、带外泄露严重、载波间干扰敏感、循环前缀开销大等问题。未来天线规模进一步增大、频段进一步提高、环境感知能力进一步增强，为无线波形的优化设计带来了新机遇。为此，本报告拟挖掘大维阵列的高空间分辨率与高频信号的多径稀疏性，提出基于空间-时延-多普勒域联合处理的时延多普勒对齐调制（Delay-Doppler Alignment Modulation, DDAM）新波形设计框架。DDAM利用逐径波束赋形在空域实现多径信号的区分，进而使能逐路径时延与多普勒补偿并实现对齐，从而极大降低信道的时延多普勒扩展，避免传统方法所需的复杂信道均衡或大维多载波传输，大幅提升无线传输效率。报告将首先介绍提出DDAM的背景与动机，进而分别介绍单载波与多载波DDAM通信、面向DDAM的信道估计方法、以及基于DDAM的通信感知一体化。最后，介绍初步的实验验证结果。