UIUC and EPFL
Landmark codes underpin reliable physical layer communication, e.g., Reed-Muller, BCH, Convolutional, Turbo, LDPC and Polar codes: each is a linear code and represents a mathematical breakthrough. The impact on humanity is huge: each of these codes has been used in global wireless communication standards (satellite, WiFi, cellular). Traditionally, the design of codes has been driven by human-ingenuity and hence the progress is sporadic. Can we automate and accelerate this process of discovering codes?
In this talk, I will talk about KO codes, a new family of computationally efficient deep-learning driven codes that outperform the state-of-the-art reliability performance on the standardized AWGN channel. KO codes beat state-of-the-art Reed-Muller and Polar codes, under the low-complexity successive cancellation decoding, in the challenging short-to-medium block length regime on the AWGN channel. We show that the gains of KO codes are primarily due to the nonlinear mapping of information bits directly to transmit real symbols (bypassing modulation) and yet possess an efficient, high performance decoder. The key technical innovation that renders this possible is the design of a novel family of neural architectures inspired by the computation tree of the Kronecker Operation (KO) central to Reed-Muller and Polar codes. These architectures pave the way for the discovery of a much richer class of hitherto unexplored nonlinear algebraic structures. And more interestingly, despite having a lot of encoding and decoding structure, KO codes exhibit striking similarity to random Gaussian codes!
Bio: Ashok is an incoming postdoctoral associate at EPFL with Prof. Michael Gastpar. He recently obtained his PhD in ECE from UIUC, advised by Prof. Pramod Viswanath. He also obtained his Masters in ECE (advised by Prof. Yihong Wu) from UIUC in 2017 and Bachelors in EE (advised by Prof. Vivek Borkar) with a minor in Mathematics from IIT Bombay in 2015. His current research interests are in theoretical and algorithmic aspects of machine learning and information theory. He is a recipient of Best Paper Award from ACM MobiHoc 2019. He is also a recipient of several graduate student awards and fellowships including Joan and Lalit Bahl Fellowship (twice), Sundaram Seshu International Student Fellowship, and is a finalist for the Qualcomm Innovation Fellowship 2018. Outside research, he likes to learn new languages, watch and read about international films, remembering movie trivia and cooking. For more details about him, please visit http://makkuva2.web.engr.illinois.edu/