【演講】2019/11/19 (二) @工四816 (智易空間),邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan) 演講「Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management」
IBM中心特別邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan)前來為我們演講,歡迎有興趣的老師與同學報名參加!
演講標題:Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management
演 講 者:Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間:2019/11/19(二) 9:00 ~ 12:00
地 點:交大工程四館816 (智易空間)
活動報名網址:https://forms.gle/vUr3kYBDB2vvKtca6
報名方式:
費用:(費用含講義、午餐及茶水)
1.費用:(1) 校內學生免費,校外學生300元/人 (2) 業界人士與老師1500/人
2.人數:60人,依完成報名順序錄取(完成繳費者始完成報名程序)
※報名及繳費方式:
1.報名:請至報名網址填寫資料
2.繳費:
(1)親至交大工程四館813室完成繳費(前來繳費者請先致電)
(2)匯款資訊如下:
戶名: 曾紫玲(國泰世華銀行 竹科分行013)
帳號: 075506235774 (國泰世華銀行 竹科分行013)
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據
聯絡方式:曾紫玲 Tel:03-5712121分機54599 Email:tzuling@nctu.edu.tw
Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.
3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.
Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.
Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
signal processing journal 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最佳解答
【Talk】Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
###@@@ You are all invited to come. @@@###
Topic:Clock Synchronization in Wireless Sensor Networks: from Traditional Estimation Theory to Distributed
Time:December 22, 2017 ( Friday, 11:00AM~12:00PM)
Venue:R210, Engineering Building 4, NCTU
交通大學工程四館210室
Speaker:Prof. Yik-Chung Wu / The University of Hong Kong
Language:Lectured in English
Abstract: In this talk, we will review the advances of clock synchronization in wireless sensor network in the past few years. We will begin with the optimal clock synchronization algorithms in pairwise setting, in which maximum likelihood (ML) estimator from traditional estimation theory is the major tool. Then, we will discuss the more challenging networkwide synchronization, in which every node in the network needs to synchronize with each other. In this case, more powerful distributed signal processing techniques are required. In particular, we will illustrate how Belief Propagation (BP), distributed Kalman Filter (KF) and Alternating Direction Method of Multipliers (ADMM) method help in solving networkwide synchronization.
Bio: Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU). He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005. From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff. Since September 2006, he has been with HKU, currently as an Associate Professor. He has been a visiting scholar at Princeton University for the summers of 2011 and 2015. His research interests are in general area of signal processing, machine learning, and communication systems, and in particular distributed signal processing and robust optimization theories with applications to communication systems and smart grid. Dr. Wu served as an Editor for IEEE Communications Letters, is currently an Editor for IEEE Transactions on Communications and Journal of Communications and Networks.
signal processing journal 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的最佳解答
AP聯盟_ 10/15-16應用處理器國際大師課程
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報名網頁:http://ccs103.kktix.cc/events/c34816ce
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活動簡介: 本課程由教育部產業創新提升人才培育計畫--高階應用處理器聯盟中心所舉辦,今年藉著國際研討會Micro-49在台灣舉辦的難得機會,合辦課程;報名課程者,除了10/16晚間的必修課程[Extending the Roofline Model: Bottleneck Analysis with Microarchitectural Constraints],還可以選修Micro-49於10/15、10/16日間課程。
全勤便能獲得結業證書,名額有限,歡迎報名參加!
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課程報名費用: NTD1,000元
參加對象:老師、學生
餐點: 附10/15(六)午餐、10/16(日)午餐,10/16(日)晚餐
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*必修課程資訊*
日期/時間: 2016/10/16(日)18:30-21:30
地點: 台北福華大飯店CR403會議室
課程簡介: http://www.spiral.net/software/bottleneck.html
講員簡歷:
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Prof. Markus Püschel
Department Head of Computer Science
ETH Zürich, Switzerland
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Markus Püschel is a Professor and currently Department Head of Computer Science at ETH Zurich, Switzerland. Before, he was a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), where he still has an adjunct status. He received his Diploma (M.Sc.) in Mathematics and his Doctorate (Ph.D.) in Computer Science, in 1995 and 1998, respectively, both from the University of Karlsruhe, Germany. From 1998-1999 he was a Postdoctoral Researcher at Mathematics and Computer Science, Drexel University. From 2000-2010 he was with CMU, and since 2010 he has been with ETH. He was an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Signal Processing Letters, was a Guest Editor of the Proceedings of the IEEE and the Journal of Symbolic Computation, and served on various program committees of conferences in computing, compilers, and programming languages. He received the Outstanding Research Award of the College of Engineering at Carnegie Mellon and the main teaching awards from student organizations of both institutions CMU and ETH. He also holds the title of Privatdozent at the University of Technology, Vienna, Austria. In 2009 he cofounded Spiralgen Inc.
His research interests include program synthesis with the goal of high performance, fast computing, algorithms, applied mathematics, and signal processing theory/software/hardware.
More information is available at http://people.inf.ethz.ch/markusp/shortcv.html
講員個人網頁:http://people.inf.ethz.ch/markusp/index.html
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*選修課程資訊*
日期/時間: 2016/10/15-10/16
地點:台北福華大飯店,會議室資訊待補
選課方式: 報名時勾選課程項目,每日上午、下午時段皆須選課
選修課程簡介: 詳細介紹請至Micro-49官網點選議程內的超連結http://www.microarch.org/micro49/program.php#workshop
會場交通資訊: http://taipei.howard-hotels.com.tw/CT_Taipei1.php?Psn=148
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*活動志工招募資訊*
招募Micro-49研討會志工,協助活動進行、夜市導覽。凡報名志工且經錄取者,可免費報名課程,限額16名(依報名順序)。
志工招募連結:https://goo.gl/forms/R7pfLOFrI0bP7T1j2
(務必完成報名課程,再行報名志工)
報名網頁:http://ccs103.kktix.cc/events/c34816ce