题目一：无线携能通信系统的能效优化研究
内容简介：随着物联网、大数据和第五代移动通信系统(5G)的快速发展，信息通信网络正在从移动互联网逐步向万物互联的智慧物联网转变。为了延长移动设备的续航时间，迫切需要整合通信技术与能源技术现有的研究成果，既满足对高效可靠信息交互的需求，又有效应对能源短缺的压力。因此，基于无线能量传输技术并将通信技术与输电技术交叉融合，产生了无线携能通信技术（SWIPT）。通过实现信息与能量的并行传输，从而充分利用宝贵的发射功率，对提高无线网络能效具有重要的实际意义。此报告针对无线携能通信系统的能效优化问题，以时间切换，功率分割，天线切换与空间切换四种技术为基础，研究设计出能效最大化的传输策略，并通过仿真对比传统方案在不同的通信场景下的能效性能，为无线携能通信技术在下一代通信网络中的应用与开发鉴定基础。
报告人：华南理工大学 唐杰 副教授
报告人简介：华南理工大学电子与信息学院院长助理，IEEE高级会员，电子学会高级会员，通信学会高级会员。2017年被评为华南理工大学 “兴华学者人才计划”启航学者。2018年被评为广东省 “珠江人才计划”青年拔尖人才。主持及参与10多项科研项目，包括国家自然科学基金项目，广东省自然科学基金项目，广州市科技计划项目等。目前已发表三大检索论文共计70余篇，其中IEEE Trans长文30余篇。Google Scholar中被他引超过1100次，h-index 17, i10-index 27。目前担任四个SCI期刊副主编，包括IEEE Access, EURASIP Journal on Wireless Communications and Networking, Physical Communications, Ad Hoc & Sensor Wireless Networks。此外，还任职IEEE Vehicular Technology Conference: VTC2018-Spring春季会议绿色通信与网络的分会主席，以及EAI International Conference on Green Energy and Networking 技术委员会委员。唐杰博士获得2011年度国家优秀自费留学生奖学金, IEEE International Conference on Computing, Networking and Communications (ICNC 2018)最佳论文奖，the 7th International Conference on Communications, Signal Processing, and Systems (CSPS2018) 最佳论文奖。
题目二：EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
内容简介：This talk introduces a decentralized algorithm for the consensus optimization problem defined over a connected network of agents. The agents collaboratively look for a common argument to minimize an aggregate cost function, which is the average of local cost functions determined by the agents' private data and objectives. During the optimization process, each agent can only communicate with its neighbors; such a computation scheme avoids a data fusion center or long-distance communication and offers better load balance to the network. We propose a novel decentralized exact first-order algorithm (abbreviated as EXTRA) to solve the consensus optimization problem. "EXACT" means that it can converge to the exact solution. EXTRA can use a fixed large step size, which is independent of the network size, and has synchronized iterations. The local iterate of every agent converges uniformly and consensually to an exact minimizer of the aggregate cost function. In contrast, the well-known decentralized gradient descent (DGD) method must use diminishing step sizes in order to converge to an exact minimizer. EXTRA and DGD have the same choice of mixing matrices and similar per-iteration complexity. EXTRA, however, uses the gradients of last two iterates, unlike DGD which uses just that of last iterate. EXTRA has the best known convergence rates among the existing first-order decentralized algorithms. If the local cost functions are convex and have Lipschitz continuous gradients, EXTRA has an O(1/k) ergodic convergence rate in terms of the first-order optimality residual. If the aggregate cost function is also (restricted) strongly convex, EXTRA converges to an optimal solution at a linear rate.
报告人：中山大学 凌青 教授
报告人简介：Qing Ling received the B.E. degree in automation and the Ph.D. degree in control theory and control engineering from the University of Science and Technology of China, Hefei, China, in 2001 and 2006, respectively. He was a Postdoctoral Research Fellow with the Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA, from 2006 to 2009, and an Associate Professor with the Department of Automation, University of Science and Technology of China, from 2009 to 2017. He is currently a Professor with the School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China. His research interests include decentralized network optimization and its applications. He is an Associate Editor for the IEEE TRANSACTIONS ON NETWORK AND SERVICEMANAGEMENT and the IEEE SIGNAL PROCESSING LETTERS. He was the recipient of the 2017 IEEE Signal Processing Society Young Author Best Paper Award as a supervisor, and the 2017 International Consortium of Chinese Mathematicians Distinguished Paper Award.
题目三：非凸优化在信号处理、统计和机器学习中的应用
内容简介：近几年，稀疏和低秩恢复在诸如信号处理、图像处理、统计学、生物信息学和机器学习等许多领域获得了广泛的研究和应用。最近的研究表明，与传统凸优化方法相比，基于非凸优化的稀疏和低秩重建方法能获得显著更优的性能。本报告将介绍基于非凸优化的稀疏和低秩重建方法在信号处理、高维统计和机器学习等领域的最新进展和应用情况，相关的应用问题包括压缩感知、稀疏回归、稀疏信号分离、稀疏主成分分析、高维协方差矩阵、高维逆协方差矩阵估计、矩阵补全和稳健主成分分析。
报告人：上海交通大学 文飞 助理研究员
报告人简介：2013年博士毕业于电子科技大学，2008--2017在空军工程大学任讲师，2015年进上海交通大学博士后流动站，2018年3月起任上海交通大学电院助理研究员，发表SCI论文20余篇，主持国家自然科学基金青年项目一项、面上项目一项，主持上海市重点科技攻关项目子课题一项，参与主研国家重点专项两项。
时 间：2018年11月15日（周四）上午10：00始
地 点：南海楼124室
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