Communications and Signal Processing Seminar
Re-engineering the Uplink for Next Generation Multiple Access
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Abstract: Current wireless infrastructures have been designed to serve human-operated devices, with popular applications such as voice telephony, Internet browsing, navigation, and music/video streaming. Much of the engineering behind existing systems has focused on developing an efficient and high-throughput downlink. Yet, the emergence of machine-type communication (MTC) is forcing a paradigm shift because unattended devices interact with their environments in fundamentally different ways compared to humans. Machine-type communication is characterized by the sporadic and fleeting nature of traffic, short payloads, and large number of transmitting users. Supporting machine-type traffic requires a re-engineering of the physical and medium access control layers in the uplink. I will provide an overview of our recent results on enhancing random access paradigms, coding, and signal processing algorithms for designing a modern uplink attuned to massive machine type communications. Our research revitalizes classical approaches such as interference cancellation, compressed sensing, interleave-division multiple access, and code division multiple access to support machine type communication. If time permits, I will discuss our recent research projects on using large language models and transformers for improving the efficiency of the uplink.
Bio: Krishna Narayanan is the Eric D. Rubin professor in the Dept. of Electrical and Computer Engineering at Texas A&M University. He was recently a visiting researcher at Qualcomm research during Spring 2023 and is currently a long-term visitor at the Simons Institute for Computing during Spring 2024. His research interests are broadly in coding theory, information theory, signal processing, and machine learning with applications to wireless communications, data storage, and data science. His current research interests are in the design of uncoordinated multiple access schemes, joint source and channel coding, coding for distributed computing, exploring connections between sparse signal recovery and coding theory, and analyzing data defined on graphs. He recently received the 2022 joint communications society and information theory best paper award, the 2020 best paper award in data storage from the IEEE communications society and a university-level distinguished teaching award in 2018. He has served as a lecturer at the North American, Australian, and East Asian schools on information theory. He was elected a Fellow of the IEEE for contributions to coding for wireless communications and data storage.
*** The event will take place in a hybrid format. The location for in-person attendance will be room 3433 EECS. Attendance will also be available via Zoom.
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Meeting ID: 914 1429 7851
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Zoom Passcode information is available upon request to Shelly Feldkamp ([email protected]).