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

内容簡介：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.

報告人：香港理工大學  劉亮  助理教授

報告人簡介：2010年獲得天津大學電子信息工程學院通信工程專業本科學位，2014年獲得新加坡國立大學大學電氣與計算機工程系博士學位。2015年至2017年在多倫多大學擔任博士後研究員，2017年至2019年在新加坡國立大學擔任博士後研究員。研究領域包括下一代無線蜂窩通信技術以及物聯網通信技術。2018年度被科睿唯安評選為全球高被引科學家。7篇論文被列為ESI高被引論文，1篇論文獲得IEEE信号處理協會（IEEE Signal Processing Society）2017年度青年作者最佳論文獎，1篇論文獲得國際會議最佳論文獎。

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

内容簡介：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.

報告人：廣東工業大學  張軍  副教授

報告人簡介：碩士生導師，系主任，廣東省高等學校“千百十工程”校級培養對象，首批“廣東工業大學優秀青年教師培養計劃”培養對象,兼任中國人工智能學會教育工作委員會委員。于2002年、2005年在湘潭大學計算機軟件與理論專業獲學士與碩士學位，2012年在華南理工大學模式識别與智能系統專業獲博士學位。2015年2月-2016年2月在美國南加州大學電子工程系從事博士後研究，主要研究方向為壓縮感知理論及應用，大數據的感知、表示與分析，面向可穿戴設備的智能信息處理等。近年來主持國家自然科學基金，廣東省自然科學基金，廣州市科技計劃等多個科研項目，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等國際期刊上發表科研論文20餘篇。

時  間：2019年10月11日（周五）上午10：00始

地  點：南海樓124室

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2019年10月10日