Abstract:
Recent years have witnessed a dramatically growing interest in machine learning (ML) methods. These data-driven trainable structures have demonstrated unprecedented empirical success in various applications, including computer vision and speech processing. The benefits of ML-driven techniques over traditional model-based approaches are twofold: First, ML methods are independent of the underlying stochastic model, and thus can operate efficiently in scenarios where this model is unknown, or its parameters cannot be accurately estimated; Second, when the underlying model is extremely complex, ML algorithms have demonstrated the ability to extract and disentangle the meaningful semantic information from the observed data. Nonetheless, not every problem can and should be solved using deep neural networks (DNNs). In fact, in scenarios for which model-based algorithms exist and are computationally feasible, these analytical methods are typically preferable over ML schemes due to their theoretical performance guarantees and possible proven optimality. The main application area where model-based schemes are typically preferable, and whose characteristics are fundamentally different from conventional deep learning applications, is receiver design in digital communications. In this talk, I will present methods for combining DNNs with traditional model-based algorithms, with the main application considered being symbol detection in digital communication systems. We will show how hybrid model-based/data-driven implementations of the Viterbi algorithm, the BCJR scheme, and the iterative soft interference cancellation method allow these fundamental techniques to be implemented without knowledge of the underlying statistical model while achieving improved robustness to uncertainty.
Bio:
Nir Shlezinger is an assistant professor in the School of Electrical and Computer Engineering at Ben-Gurion University, Israel. He received his B.Sc., M.Sc., and Ph.D. degrees in 2011, 2013, and 2017, respectively, from Ben-Gurion University, Israel, all in electrical and computer engineering. From 2017 to 2019 he was a postdoctoral researcher in the Technion, and from 2019 to 2020 he was a postdoctoral researcher at Weizmann Institute of Science, where he was awarded the FGS prize for outstanding achievements in postdoctoral research. His research interests lie in the intersection of signal processing, machine learning, communications, and information theory.
