2024-06-21
浏览次数：957报告题目: NOMA Assisted Far-Field and Near-Field Communications
报告人：丁志国（Zhiguo Ding）教授，The University of Manchester, UK
报告时间：2024年6月24日（周一）10:00-12:00
报告地点：逸夫科学馆504，华南理工大学五山校区
主持人：温淼文教授（电子与信息学院）
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摘要：
One recent advance in non-orthogonal multiple access (NOMA) is its use as an add-on in a massive multiple input multiple-output (m-MIMO) based legacy space division multiple access (SDMA) network, where spatial beams preconfigured for legacy users are used to serve additional users. As a result, the connectivity and the overall system throughput of SDMA can be improved in a low-complexity and spectrally efficient manner. For conventional SDMA networks based on far-field communications, this application of NOMA is intuitive, since the far-field spatial beams are cone-shaped. Recently near-field communications have received a lot of attention as, for high carrier frequencies and large numbers of antennas, the Rayleigh distance becomes significantly large. Unlike far-field communications, the spherical-wave channel model has to be used for nearfield communications, which motivates the use of beam-focusing, i.e., a beam is focused on not only a spatial direction but also a specific location. This talk is to focus on the resolution of near-field beam-focusing and how it can be used to facilitate the application of NOMA for the coexistence between near-field and far-field communications.
中文：非正交多址接入（NOMA）的一个最新进展是将其用作基于大规模多输入多输出（m-MIMO）的传统空分多址接入（SDMA）网络的附加设备，即利用为传统用户预先配置的空间波束为额外用户提供服务。因此，SDMA 的连接性和整体系统吞吐量可以通过低复杂度和高频谱效率的方式得到改善。对于基于远场通信的传统 SDMA 网络来说，NOMA 的应用是直观的，因为远场空间波束是锥形的。最近，近场通信受到了广泛关注，因为对于高载波频率和大量天线而言，瑞利距离变得非常大。与远场通信不同，近场通信必须使用球形波信道模型，这就需要使用波束聚焦，即波束不仅聚焦于空间方向，还聚焦于特定位置。本讲座的重点是近场波束聚焦的分辨率，以及如何利用它来促进近场和远场通信共存的 NOMA 应用。
个人简历：
Zhiguo Ding (Fellow, IEEE) received his B.Eng in Electrical Engineering from the Beijing University of Posts and Telecommunications in 2000, and the Ph.D degree in Electrical Engineering from Imperial College London in 2005. He is currently a Professor in Communications at University of Manchester and Khalifa University. Previously, he had been working in Queen's University Belfast, Imperial College, Newcastle University and Lancaster University. From Oct. 2012 to Sept. 2024, he has also been an academic visitor in Prof. Vincent Poor's group at Princeton University. Dr Ding' research interests are machine learning, B5G networks, cooperative and energy harvesting networks and statistical signal processing. His h-index is over 100 and his work receives 50,000+ Google citations. He is serving as an Area Editor for the IEEE TWC and OJ-COMS, an Editor for IEEE TVT, COMST, and OJ-SP, and was an Editor for IEEE TCOM, IEEE WCL, IEEE CL and WCMC. He received the best paper award of IET ICWMC-2009 and IEEE WCSP-2014, the EU Marie Curie Fellowship 2012-2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award 2020, IEEE SPCC Technical Recognition Award 2021, and IEEE VTS Best Magazine Paper Award 2023. He is a Web of Science Highly Cited Researcher in two disciplines (2019-2023), an IEEE ComSoc Distinguished Lecturer, and a Fellow of the IEEE.
中文：丁志国（IEEE Fellow）于 2000年获得北京邮电大学电子工程学士学位，2005 年获得伦敦帝国学院电子工程博士学位。他目前是曼彻斯特大学和哈利法大学通信专业教授。 此前，他曾在贝尔法斯特女王大学、帝国理工学院、纽卡斯尔大学和兰卡斯特大学工作。2012 年 10 月至 2024年 9 月，他还在普林斯顿大学Vincent Poor教授的研究小组担任学术访问学者。丁博士的研究兴趣是机器学习、B5G网络、合作和能量收集网络以及统计信号处理。他的h指数超过100，他的研究成果在谷歌上被引用超过50,000次。他担任 IEEE TWC 和 OJ-COMS 的区域编辑，IEEE TVT、COMST 和 OJ-SP 的编辑，并曾担任 IEEE TCOM、IEEE WCL、IEEE CL 和 WCMC 的编辑。他曾获 IET ICWMC-2009 和 IEEE WCSP-2014 最佳论文奖、2012-2014 年欧盟 Marie Curie Fellowship、2017 年 IEEE TVT Top Editor、2018 年 IEEE Heinrich Hertz Award、2018 年 IEEE Jack Neubauer Memorial Award、2018 年 IEEE Best Signal Processing Letter Award、2020 年 Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award、2021 年 IEEE SPCC Technical Recognition Award 和 2023 年 IEEE VTS 最佳杂志论文奖。他是两个学科的 Web of Science 高被引研究员（2019-2023 年）、IEEE ComSoc 杰出讲师和 IEEEFellow。