Technical Seminar: Machine Learning Aided 6G: Leveraging Sensory Data for Highly-Mobile and Reliable Communication Systems

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IEEE Communications Society, Mohawk Valley chapter cordially invites you to a (virtual) technical seminar "Machine Learning Aided 6G: Leveraging Sensory Data for Highly-Mobile and Reliable Communication Systems" by  Dr. Ahmed Alkhateeb from Arizona State University. Dr. Alkhateeb’s latest research and data sets can be found here: https://viwi-dataset.net/ and https://www.deepmimo.net/ 

Abstract: Advanced communications and signal processing for millimeter-wave (mmWave) and terahertz (THz) frequencies will be key enabling technologies for next generation wireless communications systems such as 6G and beyond. The communication over these frequency bands, however, is subject to critical challenges, such as the large training overhead associated with adjusting the narrow beams and the high sensitivity to blockages. These challenges make it difficult for mmWave/THz MIMO systems to support applications like virtual/augmented reality and vehicular communications that have high mobility and strict reliability/latency constraints. In this talk, I will present DeepSense 6G, a large-scale real-world dataset that enables multi-modal sensing and communication deep learning research. I will go over the dataset structure and the available scenarios. Then, I will present some of the applications and machine learning tasks enabled by the dataset such as position, radar, LiDAR, and camera aided beam prediction, and sensing-aided proactive blockage prediction. Finally, I will provide a brief overview of other relevant research directions, including the interplay between communications, imaging, and localization at above 100GHz, and the integration of large intelligent surfaces in future wireless systems.

Bio: Ahmed Alkhateeb received his B.S. degree (distinction with honor) and M.S. degree in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical Engineering from The University of Texas at Austin, USA, in August 2016. In Sept. 2016- Dec. 2017, he was a Wireless Communications Researcher at the Connectivity Lab, Facebook, in Menlo Park, CA. He joined Arizona State University (ASU) in Spring 2018, where he is currently an Assistant Professor in the School of Electrical, Computer, and Energy Engineering. He has held R&D internships at FutureWei Technologies (Huawei) in Chicago, IL, and Samsung Research America (SRA) in Dallas, TX. His research interests are in the broad areas of wireless communications, communication theory, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award 2021 to support his research on leveraging machine learning for large-scale MIMO systems.

 

 
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  Date and Time

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  • Date: 22 Nov 2021
  • Time: 11:00 AM to 12:00 PM
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  • Rome, New York
  • United States



  Speakers

Dr. Ahmed Alkhateeb

Topic:

Machine Learning Aided 6G: Leveraging Sensory Data for Highly-Mobile and Reliable Communication Systems

Abstract: Advanced communications and signal processing for millimeter-wave (mmWave) and terahertz (THz) frequencies will be key enabling technologies for next generation wireless communications systems such as 6G and beyond. The communication over these frequency bands, however, is subject to critical challenges, such as the large training overhead associated with adjusting the narrow beams and the high sensitivity to blockages. These challenges make it difficult for mmWave/THz MIMO systems to support applications like virtual/augmented reality and vehicular communications that have high mobility and strict reliability/latency constraints. In this talk, I will present DeepSense 6G, a large-scale real-world dataset that enables multi-modal sensing and communication deep learning research. I will go over the dataset structure and the available scenarios. Then, I will present some of the applications and machine learning tasks enabled by the dataset such as position, radar, LiDAR, and camera aided beam prediction, and sensing-aided proactive blockage prediction. Finally, I will provide a brief overview of other relevant research directions, including the interplay between communications, imaging, and localization at above 100GHz, and the integration of large intelligent surfaces in future wireless systems.

Biography:

Ahmed Alkhateeb received his B.S. degree (distinction with honor) and M.S. degree in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical Engineering from The University of Texas at Austin, USA, in August 2016. In Sept. 2016- Dec. 2017, he was a Wireless Communications Researcher at the Connectivity Lab, Facebook, in Menlo Park, CA. He joined Arizona State University (ASU) in Spring 2018, where he is currently an Assistant Professor in the School of Electrical, Computer, and Energy Engineering. He has held R&D internships at FutureWei Technologies (Huawei) in Chicago, IL, and Samsung Research America (SRA) in Dallas, TX. His research interests are in the broad areas of wireless communications, communication theory, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award 2021 to support his research on leveraging machine learning for large-scale MIMO systems.





Agenda

Technical seminar followed by Q&A session.