Prof. Sundeep Rangan (NYU)
Communication in the millimeter-wave (mmWave) bands offers the potential for massive data rates at low latencies, but is fraught with challenges including high device power consumption, complex propagation, rapid channel dynamics, and need to support beam tracking. At the same time, the computational and storage capabilities of network nodes and devices is rapidly increasing, making available a huge amount of site-specific or device-specific data that can be exploited by machine learning methods. This talk will review some recent works in data-driven, ML methods for mmWave communication including neural network generative channel models and LSTM beam tracking. We will also discuss ongoing work in using mmWave to enable mobile AI in computational offloading for robotics and mobile visual perception systems.
Bio:
Dr. Rangan received the B.A.Sc. at the University of Waterloo, Canada and the M.Sc. and Ph.D. at the University of California, Berkeley, all in Electrical Engineering. He has held postdoctoral appointments at the University of Michigan, Ann Arbor and Bell Labs. In 2000, he co-founded (with four others) Flarion Technologies, a spin-off of Bell Labs, that developed Flash OFDM, the first cellular OFDM data system and pre-cursor to 4G cellular systems including LTE and WiMAX. In 2006, Flarion was acquired by Qualcomm Technologies. Dr. Rangan was a Director of Engineering at Qualcomm involved in OFDM infrastructure products. He joined the ECE department at NYU Tandon (formerly NYU Polytechnic) in 2010. He is a Fellow of the IEEE and the Associate Director of NYU WIRELESS, an industry-academic research center on next-generation wireless systems.
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Wireless ML Seminars is a series of lectures focused on Machine Learning in the wireless space. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The seminar series is curated by Prof. Jeff Andrews and Prof. Hyeji Kim from the Wireless Networking and Communications Group at the University of Texas at Austin.
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