Skip to main content
abstract blue background

Education & Training

Online Course

29 - 30 June 2022 - 9:00 am to 12:30 pm EDT

Registration closes on 27 June 2022 at 5:00pm EDT

Online via WebEx

Price

$279 IEEE ComSoc member
$349 IEEE member
$459 non-member

Not a member?  Join ComSoc Today
IEEE Members can add ComSoc to their membership.

 

CEU Credits:  0.6 CEUs

Course Description

Artificial intelligence (AI) and machine learning (ML) are considered as the of the most important universal technologies of our era, like electricity and the combustion engine. Particularly deep learning has led to many recent breakthroughs in various domains, such as computer vision, natural language processing, and speech recognition. It is therefore natural to ask what role AI/ML will play for wireless communications? This course tries to shed some light on this question by introducing key concepts of deep learning and their applications to problems in communications, ranging from channel estimation, over a full neural OFDM receiver, to an entirely neural network-based communications system that does not use any traditional algorithm. For now, deep learning for communications is a novel field that offers many attractive interdisciplinary research questions at the interface between machine learning, communications engineering, information theory, as well as hardware design.

As one of the hottest topics currently in our field, it is expected that machine learning will play an increasingly important role in the future evolutions of 5G as well as the development of 6G.  Hence, this course is a great opportunity to learn about the cutting-edge research in communications and deep learning with a particular focus on practical implementation with state-of-the-art deep learning libraries.

Developed and taught by one of the pioneers of this field, participants will be guided to relevant resources and provided with a realistic assessment of the impact of machine learning on the future of our industry.  Participants will get access to Jupyter notebooks with code examples that will enable them to deepen their understanding of the topic and design their own experiments.

Who Should Attend

The course is intended for engineers and researchers working in the telecommunications industry. It is also of interest to graduate students, postdocs, and professors who want to get an introduction to the topic of deep learning for the physical layer.

Level of Instruction: Introductory/Intermediate

Prerequisites

This introductory/intermediate course on deep learning for the physical layer has the following prerequisites:

  • Solid background in digital communication systems, especially the physical layer (OFDM, MIMO, modulation, detection, estimation, channel coding)
  • Background on basic information theory, signal processing, and wireless communications.
  • Basic knowledge of machine learning and, particularly, deep learning is good to have but not a prerequisite.
  • Knowledge of the Python programming language as well as TensorFlow or PyTorch are beneficial

Instructor

Jakob Hoydis

Jakob Hoydis

Principal Research Scientist

NVIDIA

Learning Objectives

After this course, participants will be able to:

  • Identify when it makes sense to use machine learning to solve a problem.
  • Be aware of the current 3GPP standardization efforts with respect to AI/ML.
  • Recognize the most important state-of-the-art neural network architectures relevant
  • to the physical layer.
  • Setup simple experiments to solve physical layer problems using neural
  • networks.
  • Understand the idea of end-to-end learning and related challenges.
  • Describe the idea of Transformers and Graph neural networks.

Course Content

  • Part I - Introduction & Primer on Machine Learning and Deep Learning
    • Introduction
      • What is ML / AI?
      • When (not) to use ML?
      • What can ML do for communications?
      • 3GPP Rel. 18 ML/AI activities
      • Components of an ML System
    • Deep Learning Basics
      • Feed-forward neural networks
      • Activation functions
      • Gradient descent & Backpropagation
      • Universal approximation theorem
      • Capacity, over- & underfitting
      • Regularization
    • Convolutional Neural Networks
      • Convolutional layers
      • Stride, pooling
      • Receptive field and dilation
      • Depth-wise separable convolutions
      • Fully convolutional networks & ResNets
  • Part II - Applications of Deep Learning for Communications
    • Introduction to the TFComm Software Library
  • DL at the Receiver
    • Example: Neural OFDM Receiver
    • Example: Autoencoders for channel feedback compression
  • End-to-end Learning
    • What is E2E learning?
    • Bit-Metric Decoding Rate
    • Geometric shaping
    • Gradient Estimation
    • Example: Train your first Autoencoder
    • Turbo Autoencoders
    • Waveform learning
  • Overview of Advanced Topics
    • Graph neural networks
      • Example: Learned BP Decoder
    • Transformers
      • Example: MIMO Detection

Course Materials

Each registered participant receives a copy of instructor slides and access to the recording of the course for 20 business days after the live lecture. Earn 0.6 IEEE Continuing Education Units for participating.

Upon registration, you will automatically be emailed the WebEx invitation for the course session, but you will also be sent a reminder message to join the WebEx session prior to the start of the course. Course materials will be emailed to you and will be available for download from the WebEx session page for this course, the day prior to the scheduled course date.

Course Cancellation and Refund Policy: Requests for online course cancellations must be received 3 business days prior to the course date for a full refund. Once course materials have been shared with a participant, a cancellation request cannot be accommodated.

Contact Us

For general inquiries and technical support, contact Tara McNally, Certification and Professional Education Manager.

Review the system requirements for WebEx.
Test your browser by joining a meeting.