Steering Board
Prof. Eduard Hovy
Carnegie Mellon University
Prof. Tony Quek,
University of Technology and Design

Workshop Chairs

Dr. Yi Wang [SM’11] is currently a principal engineer at Huawei Technologies Co., Ltd. in Shanghai. He received the M.S.E.E and Ph.D degrees in information engineering department from Beijing University of Posts & Telecommunications, China, in 1997 and 2000 respectively. He has worked at Tsinghua University and the University of Kiel in Germany as post-doctor. Since 2005 he joined Huawei Technologies Co., Ltd. he led a series of research projects including beyond 3G, superposition coding for LTE-advanced system, distributed antenna system, cloud RAN, massive MIMO and 5G mmWave communications. Currently he is leading 5G multi-band communications research. Dr. Yi Wang owns 60+ patents and 70+ publications. Many patents have been realized in LTE product or used in 3GPP and IEEE802.11 standards. He has been involved in as series of research organizations including Moible VCE, WWRF, 3GPP, IEEE802, and IEEE conferences.

Xiaojie Wang received his Ph.D. degree from Beihang University in 1996. He is a professor and director of the Centre for Intelligence Science and Technology at Beijing University of Posts and Telecommunications. His research interests include Natural Language Processing and multi-modal cognitive computing. He is an executive member of the council of Chinese Association of Artificial Intelligence, director of natural language processing committee. He is a member of council of Chinese Information Processing Society and member of Chinese Processing Committee of China Computer Federation. He has published more than 100 papers including an ACM Multimedia best paper award finalist.

Workshop description

With the developments and applications of wireless communications, more and more applications require advanced radio transmission technology (RTT) to reach the goal of low-power, high spectrum efficiency and flexible to multiple scenarios such as mobile broadband, ultra reliable communications, internet-of-things. Recently intelligent optimization and self-learning algorithms are widely studied. Evolution algorithm is to find maximum point with complex non-continuous cost functions by biologic technology such as genetic algorithm and particle swarm optimization. Self-learning algorithm is lighted up with the success of machine-learning in artificial intelligent field.  With the strong requirements to RTT and fruitful achievements in evolution and self-learning algorithm (ESLA), it is foreseen that applying ESLA to RTT may help solve some challenges in wireless communications.

This workshop will provide a forum for both industry and academia to exchange views and visions. Topics of interest include but are not limited to the following:

1. Overview of intelligent optimization

2. Overview of machine-learning algorithms

3. Massive MIMO with ESLA

4. Position/location estimation with ESLA

5. Radio resource management with ESLA

6. Signal detection with ESLA

7. Channel estimation and tracking with ESLA

8. Channel coding and decoding with ESLA

9. Power control with ESLA

Important dates

Manuscript submissions:   July 27, 2017

Notifications:             August 11, 2017

Final papers:             August 18, 2017

Workshop day:           October 8/13, 2017 (TBD)

 Submission entrance:http://edas.info/N23914