Dr. Nir Shlezinger awarded ERC Starting Grant
Dr. Nir Shlezinger of the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev has been awarded the prestigious ERC Starting Grant for 2024. The grant provides up to 1.5 million euros over five years.
Dr. Shlezinger of the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev has been awarded the prestigious ERC Starting Grant for 2024. The grant provides up to 1.5 million euros over five years.
Dr. Shlezinger’s research focuses on the link between machine learning, signal processing, and wireless communications. The traditional approach in these domains was based on the programmer's knowledge, translated into a distinct set of commands according to which decisions were executed. A few years ago, machine learning, especially deep learning, changed the rules of the game by creating systems that are not designed in advance to carry out a specific operation, but rather, learn on their own to make decisions based on examples. He tries to link the traditional approach to deep learning to benefit from the best of both worlds.
His ERC project, entitled, Flexible Lightweight AI-Aided Receivers (FLAIR), aims at bringing AI (Artificial Intelligence) to wireless communications.
AI is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains. The main challenges arise from the dynamic nature of wireless communications, which causes continual changes to the data distribution, combined with the limited power and computational resources of wireless devices. These challenges impair conventional AI based on offline trained massive DNNs.
His ambitious goal is to introduce a new form of flexible lightweight AI that is particularly tailored for wireless communications. The approach is based on holistically revisiting the three fundamental pillars of AI – the architecture, dictating the family of learned mappings; the training algorithm that tunes the architecture; and the data based on which learning is carried out. Accordingly, he and his students will focus on three objectives – 1) design trainable receiver architectures that are lightweight and support adaptation to rapid channel variations; 2) establish a new learning paradigm that deviates from conventional training, and is based on viewing continual learning as a dynamic system; and 3) propose techniques to accumulate online data sets that are sufficiently informative for learning purposes while being small enough not to induce notable complexity in training.
“This is a fundamental departure from conventional deep learning, based on highly parameterized DNNs trained with massive data sets using lengthy learning procedures. Our preliminary data show that this paradigm shift achieves substantial performance, robustness, and complexity gains over conventional deep receivers. The project will transform how communications systems are studied, and profoundly impact a multitude of applications that rely on wireless communications,” he explains.
The ERC, set up by the European Union in 2007, is the premier European funding organization for excellent frontier research. It funds outstanding researchers to run groundbreaking projects. The ERC offers four core grant schemes: Starting Grants, Consolidator Grants, Advanced Grants and Synergy Grants. With its additional Proof of Concept Grant scheme, the ERC helps grantees to bridge the gap between their pioneering research and early phases of its commercialization.
