Ad Hoc Committee on Deep Learning for Wireless Communications

7 May, 2019 | Member Updates
Article by Tony Q.S. Quek, Founding Chair

Recent breakthroughs in AI and machine learning—deep neural networks, the availability of powerful computing platforms, and big data—have provided new technologies to perform tasks that once seemed impossible.

With this technological revolution, it is clear that we may need to rethink how and whether wireless communication systems should be designed differently.

As such, it is timely to set up an ad hoc committee on Deep Learning for Wireless Communications with the kind support and advice from VTS VP—Mobile Radio Jae Hong Lee. As Founding Chair, I will introduce myself, and share my plans for this committee.

Currently, I am a tenured Associate Professor with the Singapore University of Technology and Design (SUTD). I also serve as Acting Head of ISTD Pillar, Deputy Director of SUTD-ZJU IDEA, and Sector Lead of SUTD AI Program. My current research topics include wireless communications and networking, security, big data processing, network intelligence, and IoT. I am a Distinguished Lecturer of IEEE Communications Society and a Fellow of IEEE.

The main goal of this committee is to gather common interests and establish a technical community within VTS to explore how Deep Learning can be applied for future wireless communications design and perhaps this can be the main ingredient of 6G.

To achieve this, we plan to organize tutorials, workshops, and panels related to this topic at future VTCs. Moreover, a special issue in IEEE Vehicular Technology Magazine will be planned in the near future, to consolidate the latest research results in this area.

To ensure the success of this committee, I graciously request your support: please share your views and contribute volunteer support in any capacity. Please feel free to contact me via tonyquek@sutd.edu.sg.

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One comment on “Ad Hoc Committee on Deep Learning for Wireless Communications

  1. Jen-Yeu Chen (Cosmos) on

    Thank Good Efforts from Tony ! By your organization of this committee and the activities planed in the near future, solid developments on this topic should be prosperously output soon! I just want to suggest that in addition to Deep Learning, some other ML schemes might also be considered in IoV (or VTS) setting , for example, Distributed Learning among Vehicles (by V2V communications), or Edge/Fog computing/Learning issues on acquiring data for training and implementing the rules on the network edges.

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