Shervin Vakili

Shervin Vakili joined the Énergie Matériaux Télécommunications Research Centre at the Institut National de la Recherche Scientifique (INRS-EMT) as an Assistant Professor in February 2022. He received his Ph.D. in computer engineering from Polytechnique Montréal, Canada, in 2014. After completing his Ph.D., he continued his research in computer architecture as a postdoctoral fellow, research associate, and professional researcher at Polytechnique Montréal and École de Technologie Supérieure, Montréal, Canada, for over five years. He has extensive research experience in designing ASIC- and FPGA-based computer architectures for applications in artificial intelligence, telecommunications, and computer networks. Dr. Vakili founded and currently directs the Edge Computing, Communication, and Learning (ECCoLe) lab at INRS-EMT.

His research interests include high-efficiency computer architectures, machine learning for wireless signal processing, machine learning hardware and Edge computing.

SDRobot: Software-Defined Radio-Enabled Robot Developed at the ECCoLe Lab

Founded in May 2022, Edge Computing Communication and Learning (ECCoLe) lab at INRS-EMT center is a research team dedicated to pioneering novel hardware architectures and computing techniques for high-efficiency Edge computing.

Current research at ECCoLe primarily focuses on two key areas:

1- The design of cost-effective and energy-efficient hardware accelerators for deep machine learning methods,

2- The development of hardware-aware deep learning model design and optimization techniques.

We take pride in our relatively small yet highly productive team, which has achieved significant progress and accomplishments in the short existence of ECCoLe lab. In addition to our expertise in computer architecture and advanced machine learning methods, our ECCoLe team possesses a strong foundation in wireless signal processing and maintains a robust collaboration with the telecommunication research groups at INRS-EMT. This synergy positions ECCoLe uniquely to advance research in utilizing machine learning methods within modern wireless communication systems.

TEL-302: Machine Learning for Wireless Communication

Description:

This course focuses on the expanding applications of advanced machine learning (ML) techniques in wireless communication systems. The exponential growth in the number of wirelessly connected devices has led to spectrum congestion and interference issues. Simultaneously, the increasing demand for higher communication data rates presents another critical challenge. Intelligent wireless communication techniques are gaining increasing attention as a solution to these issues.

Machine learning has demonstrated its potential to address several pivotal problems in advanced wireless communication techniques. This course provides a comprehensive review of the most recent breakthroughs and discoveries related to the use of machine learning models and techniques in tackling emerging wireless communication problems. It places particular emphasis on challenges encountered at the physical layer.

Objectives:

The primary objective is to review the latest achievements in using state-of-the-art machine learning models to address emerging wireless communication problems. It provides deep insights into the benefits and challenges associated with the utilization of different machine-learning techniques for each distinct problem. Moreover, this course outlines a roadmap that highlights the prospects and directions for machine learning-enabled wireless communications. Finally, it delves into an examination of the principal challenges encountered when deploying machine learning models for these problems, especially within edge applications, and explores the promising solutions to address them.

Contents:

• Principles of Supervised Learning, Reinforcement Learning, Unsupervised Learning
• Deep Machine Learning: Benefits and Challenges
• Transfer Learning, Adaptive Learning and Federated Learning
• Machine Learning for Wireless Signal Detection
• Machine Learning for Spectrum Sharing and Cognitive Radios
• Machine Learning for Channel Estimation and Prediction
• Machine Learning for Beamforming
• Data Augmentation and Generative AI in Machine Learning-assisted Wireless Signal Processing
• Collaborative and Adaptive Learning Solutions
• Real-Time Computing and Computational Complexity Challenge
• Hyperparameter Tuning, Quantization, Pruning