学 术

分享到微信 ×
打开微信“扫一扫”
即可将网页分享至朋友圈
博士后学术沙龙(第72期)
文:通信抗干扰技术国家级重点实验室 来源:抗干扰实验室 时间:2022-09-29 14185

  为搭建我校博士后之间的学术交流平台,促进学术水平提升,学校博士后管理办公室组织开展博士后学术沙龙活动。本次沙龙由我校博士后王斌、张倩倩、龙睿哲和黄驿轩分享其研究成果,诚挚邀请感兴趣的师生参加。

  一、时 间:2022年9月29日(周四)14:00

  二、地 点:线上会议-腾讯会议室:210-683-395

  三、主办单位:电子科技大学博士后管理办公室

  四、承办单位:通信抗干扰技术国家级重点实验室  电子科技大学博士后联谊会

  五、活动安排:

  报告一:

  (1)主 题:Confederated Learning: Federated Learning with Networked Edge Servers

  (2)主讲人:王斌  通信抗干扰技术国家级重点实验室博士后 

  (3)交流内容:

  The tremendous success of machine learning is inseparable from the help of huge data sets. Most conventional machine learning algorithms are implemented in a centralized manner, requiring the training data to be collected and processed in a central node. However, securely aggregating heterogeneous data dispersed over various data sources or organizations is a non-trivial task. The challenges concurrently arise from a privacy-protecting perspective. Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a framework within which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a framework has limited scalability as the number of devices grows. In this talk, we introduce a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop an alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of user devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers.

  (4)主讲人简介:

  B. Wang received the Ph.D. degree from the University of Electronic Science and Technology of China in 2021. He currently works as a Postdoctoral Fellow in the National Key Laboratory of Science and Technology on Communication of UESTC. Dr. Wang is working in the field of federated learning. His main research interests include federated optimization; stochastic optimization as well as optimization modeling & algorithms for wireless communications.

  报告二:

  (1)主 题:Mutualistic transmission theory and resource allocation method in symbiotic radios

  (2)主讲人:张倩倩  通信抗干扰技术国家级重点实验室博士后 

  (3)交流内容:

  In recent years, due to the mutualistic sharing of spectrum and energy, symbiotic radios have attracted considerable attention and provided a potential solution to the spectrum and energy issues in 6G. In symbiotic radio, the secondary system uses the spectrum and radio frequency signal of the primary system for passive backscatter transmission. Meanwhile, the secondary transmission provides additional multipath to the primary system, which is expected to improve the performance of the primary system. In this talk, the basic concepts and technical principles of symbiotic radio are firstly discussed. Second, the clustering based receiver design of the symbiotic radio is discribed. Third, the mutualistic condition between the primary and secondary systems in symbiotic radio is discussed. Finally, the applications of the reconfigurable intelligent surface (RIS) in symbiotic radio are described, and the benefits brought by the introduction of RIS for symbiotic radios are summarized.

  (4)主讲人简介:

  Qianqian Zhang received the B.S. degree in communication engineering from Jilin University, Changchun, China, in 2016, and the Ph.D. degree from the National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2021. She holds a post-doctoral position with UESTC. From 2019 to 2020, she was a Visiting Student with the Department of Electrical Engineering, Princeton University. Her current research interests include symbiotic radio, ambient backscatter communication, transceiver design for Internet-of-Things, and machine learning for wireless communications. She received the IEEE GLOBECOM Best Paper Award in 2021 and the Journal of Communications and Information Networks (JCIN) Best Paper Award in 2021.

  报告三:

  (1)主 题:共生无线通信系统资源配置机制与算法研究

  (2)主讲人:龙睿哲  通信抗干扰技术国家级重点实验室博士后

  (3)交流内容:

  现代无线通信是我国智能化社会建设的重要引擎。随着智慧医疗、智能制造和智能交通等新型智能化应用的不断涌现,无线通信亟需借助多种通信系统以支撑多样化业务并提升核心技术指标,其发展进而呈现出多种通信系统共存的态势。在频谱与能量等通信资源受限的情况下,多种通信系统需要进一步利用资源共享来提高资源利用效率。因此,如何通过资源共享实现各种系统间的高效共存已成为无线通信研究的重点。共生无线通信(Symbiotic Communication)提供了一类实现主被动通信系统高效共存的新型无线通信技术。在共生无线通信中,被动通信系统的反射设备(Backscatter Device, BD)利用主动通信系统射频信号进行反射传输,主动通信系统也可从被动通信系统提供的额外多径分量中获益。通过共享频谱、能量与基础设施资源,主被动通信系统有望实现双方均获益的高效共存。为进一步提升共生无线通信系统性能,需探寻关键传输参数对主被动通信系统传输性能和共存关系的影响,引入新型BD设计以增强传输,并开展各类共生无线通信系统资源配置机制与算法研究。

  首先研究不同共生无线通信系统传输参数设置下的主被动通信系统传输性能和共存关系,其次引入全双工型和放大增强型BD设计,开展共生无线通信系统资源配置机制与算法研究,主要包括以下三部分内容:(1)共生无线通信系统设计原理及速率分析;(2)全双工型共生无线通信系统设计及资源配置算法;(3)多天线增强型共生无线通信系统设计及资源配置算法。

  (4)主讲人简介:

  龙睿哲,2022年博士毕业于电子科技大学通信与信息系统专业。现为电子科技大学通信抗干扰技术国家级重点实验室博士后,研究方向为共生无线通信、反射通信、智能反射表面辅助通信。

     报告四:

  (1)主 题:Waveform Design and Signal Processing for Fusion of Communications and Radar

  (2)主讲人:黄驿轩 通信抗干扰技术国家级重点实验室博士后 

  (3)交流内容:

  With the development of electronic and information technology, wireless communication and radar detection tend to be more and more similar in spectrum employment, hardware architecture, system architecture and signal processing. Considering spectrum efficiency, hardware cost-effectiveness and new business applications, the demand of the internet of vehicles (IoV) for the fusion of communication and radar (RadCom) technology will keep growing in the future. Since it can meet the requirements of high-speed communication and small range resolution and can eliminate the mutual interference between communication and radar waveforms, the orthogonal frequency division multiplexing (OFDM) RadCom waveform within waveform sharing mode has become one of the best candidate RadCom waveforms. Aiming at the problems of high peak to average power ratio (PAPR) faced by OFDM RadCom waveform within waveform sharing mode, RadCom waveform design under the condition of spectrum interference and RadCom multi-user networking in time-frequency domain, my research mainly focuses on OFDM RadCom waveform design and signal processing within waveform sharing mode. The specific research contents are as follows:

  (1) Aiming at the inherent high PAPR problem of traditional OFDM RadCom waveform, a constant envelope orthogonal frequency division multiplexing (CE-OFDM) RadCom waveform design method based on phase modulation is proposed.

  (2) Aiming at the problem that traditional OFDM RadCom system cannot complete both communication and radar functions when existing burst spectrum interference with unknown number and width in the environment, a non-contiguous orthogonal frequency division multiplexing (NC-OFDM) RadCom waveform design method based on spectrum sensing is proposed.

  (3) Aiming at the problem of OFDM RadCom multi-user networking, a time-frequency domain multi-user networking approach for OFDM RadCom based on continuous-wave time division multiple address (TDMA) is proposed.

  (4)主讲人简介:

  Y. Huang received the Bachelor degree and Ph.D. degree from the University of Electronic Science and Technology of China (UESTC), in 2015 and 2022, respectively. He currently works as a Postdoctoral Fellow in the National Key Laboratory of Science and Technology on Communications of UESTC. Dr. Huang is working in the field of the fusion of communications and radar (RadCom). His main research interests include RadCom waveform design; 5G/6G RadCom signal process; multi-user RadCom scheme.


                        电子科技大学博士后管理办公室

                            2022年9月27日


编辑:张闻起  / 审核:林坤  / 发布:林坤