講座題目：機器學習輔助的無線技術：過去、現狀與未來 

　　報  告 人♝：教授Lajos Hanzo

　　時   　間👱🏽‍♀️：2019年5 月30日下午14:30-16:30

　　地　   點：信息科學實驗樓202報告廳 

　　主辦單位🥎：意昂平台、信息與電子學院

　　報名方式：登錄意昂官网微信企業號---第二課堂---課程報名中選擇“【百家大講堂】第200期：機器學習輔助的無線技術📍🤰🏽：過去、現狀與未來  ”

 

【主講人簡介】

 

Lajos Hanzo教授：於1983年在布達佩斯技術大學獲得博士學位👚，於2004年在英國南安普敦大學被授予榮譽博士學位🍛，目前是英國皇家工程院院士，電子電氣工程師協會會士(IEEE Fellow)，工程技術協會會士(IET Fellow)，IEEE通信學會Governor，IEEE車載技術學會Governor🧑🏼‍💻，清華大學信息科學與技術國家實驗室講席教授🧑‍💼，在IEEE期刊及會議上發表文章超過1700篇，著有20多本學術著作💆🏻‍♀️。Hanzo教授擔任了多次國際學術會議總主席或技術程序委員會主席🧑🏼‍🤝‍🧑🏼，他在2008年-2012年期間還擔任了IEEE出版社主編。

主講人簡介（英文）

Lajos Hanzo received his Doctorate in 1983 from the Technical University of Budapest and was awarded the Doctor of Science degree from the University of Southampton in 2004. He is a Fellow of the Royal Academy of Engineering (FREng), FIEEE, FIET. He is a Govenor of the IEEE ComSoc and IEEE VTS. He is also a Chaired Professor at Tsinghua University. Prof. Hanzo has co-authored 20 books and published more than 1700 research papers in at IEEE Xplore. He has also organized and chaired major IEEE conferences. During 2008 to 2012, he was the Editor-in-Chief of the IEEE Press.

 

【講座信息】

雖然對未來的研究方向進行預測充滿著挑戰性，但同時也給予了我們“未蔔先知”的特權👷🏻‍♂️。本次報告將從更廣泛的視角出發，以（A）性能指標🧑‍🎓，（B）設計和優化工具以及（C）解決方案和應用這三個不同的角度探索未來的無線技術🙅🏻‍♀️。目前學術界的研究熱點是設計基於帕累托最優的無線系統。對於一個帕累托最優系統而言💹，只能通過降低系統的一部分效能以改進上述提到的性能指標。為設計滿足帕累托最優的系統👐🏽，必須采用基於生物啟發、機器學習和量子搜索輔助的優化技術，並借助多元優化算法🥔，而這些算法具有巨大的搜索空間⛑️。接下來讓我們探討如何解決所面臨的這些挑戰！

內容簡介（英文，如有）

It is always a challenge, but also a privilege to embark on `crystal-ball gazing', when we try and predict the directions of frontier-research beyond the horizon. So, valued Colleague, let's just just that together! Commencing on a broad note, let's adopt a light-hearted three-pronged approach, touching upon A/ the performance metrics; B/ the design and optimization tools and C/ compelling solutions/applications; Our research community is now poised to enter the era of designing Pareto-optimum systems, where - by definition - it is only possible to improve any of the above-mentioned metrics at the cost of degrading some of the others. Sophisticated bio-inspired, machine-learning and quantum-search assisted optimization techniques will have to be used for designing Pareto-optimum solutions with the aid of multi-component optimization algorithms, which tend to have a large search-space. We have some exciting research challenges ahead...!