题目一：Machine-Type Communications for Internet of Things: Roadmap to a Connected World

内容简介：Last decades stood witness to the remarkable achievement of the wireless technologies in terms of connecting the people all over the world. Recently, there are growing interests from both academia and industry in another direction – to provide ubiquitous connectivity among machines. Such a paradigm shift from human-type communications (HTC) toward machine-type communications (MTC) is mainly driven by the emergence of Internet of Things (IoT). To pave the way for IoT, in this talk we will focus on how to embed MTC’s unique features of massive connectivity, ultra-reliability, and low latency into the 5G networks. First, we will study a massive IoT connectivity scenario in which a massive number of IoT devices can potentially connect to the network, but at any given time only a fraction of potential devices are active due to the sporadic device traffic. Based on the state evolution of the approximate message passing (AMP) algorithm, we analytically prove that the device activity detection error probability goes down to zero as the number of antennas at the base station goes to infinity. Therefore, massive multiple-input multiple-output (MIMO) ideally suits massive IoT connectivity. Second, we will study an industry automation scenario in which the controller has to send the commands to the actuators in a reliable and timely manner. Based on the observation that tasks in the factories are generally assigned to groups of actuators working in close proximity, we investigate a two-phase protocol, in which the controller sends the commands to the carefully selected group leaders in the first phase, which relay the commands to their group members via the device-to-device (D2D) communication techniques in the second phase. Such a scheme is shown to significantly improve the reliability of the low latency communications in the factories.

报告人：香港理工大学  刘亮  助理教授

报告人简介：2010年获得天津大学电子信息工程学院通信工程专业本科学位，2014年获得新加坡国立大学大学电气与计算机工程系博士学位。2015年至2017年在多伦多大学担任博士后研究员，2017年至2019年在新加坡国立大学担任博士后研究员。研究领域包括下一代无线蜂窝通信技术以及物联网通信技术。2018年度被科睿唯安评选为全球高被引科学家。7篇论文被列为ESI高被引论文，1篇论文获得IEEE信号处理协会（IEEE Signal Processing Society）2017年度青年作者最佳论文奖，1篇论文获得国际会议最佳论文奖。

题目二：Support Recovery From Noisy Random Measurements Via Weighted L1 Minimization

内容简介：The problem of estimating a high-dimensional vector from limited observations arises in many applications. To solve this problem, a classical method in compressive sensing (CS) is the ℓ1 minimization. Herein, we analyze the sample complexity of general weighted ℓ1 minimization in terms of support recovery from noisy underdetermined measurements. This analysis generalizes prior work for standard ℓ1 minimization by considering the weighting effect. We state explicit relationship between the weights and the sample complexity such that i.i.d random Gaussian measurement matrices used with weighted ℓ1 minimization recovers the support of the underlying signal with high probability as the problem dimension increases. This result provides a measure that is predictive of relative performance of different algorithms. Motivated by the analysis, a new iterative weighted strategy is proposed. In the Reweighted Partial Support (RePS) algorithm, a sequence of weighted ℓ1 minimization problems are solved where partial support recovery is used to prune the optimization; furthermore, the weights used for the next iteration are updated by the current estimate. RePS is compared to other weighted algorithms through the proposed measure and numerical results, which demonstrate its superior performance for a spectrum occupancy estimation problem motivated by cognitive radio.

报告人：广东工业大学  张军  副教授

报告人简介：硕士生导师，系主任，广东省高等学校“千百十工程”校级培养对象，首批“广东工业大学优秀青年教师培养计划”培养对象,兼任中国人工智能学会教育工作委员会委员。于2002年、2005年在湘潭大学计算机软件与理论专业获学士与硕士学位，2012年在华南理工大学模式识别与智能系统专业获博士学位。2015年2月-2016年2月在美国南加州大学电子工程系从事博士后研究，主要研究方向为压缩感知理论及应用，大数据的感知、表示与分析，面向可穿戴设备的智能信息处理等。近年来主持国家自然科学基金，广东省自然科学基金，广州市科技计划等多个科研项目，在IEEE Trans. Signal Proc., IEEE Trans. Instrumentation and Measurement, IEEE J. Biomedical and Health Informatics, IEEE Trans. Biomedical Eng., IEEE Signal Proc. Letters, IEEE Photonics Technology Letters，IEEE Wireless Communications Letters等国际期刊上发表科研论文20余篇。

时  间：2019年10月11日（周五）上午10：00始

地  点：南海楼124室

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2019年10月10日