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Online Course

10 - 11 May 2023 - 9:00 am to 12:30 pm EDT

Registration closes on 8 May 2023 at 5:00pm EDT

Online via WebEx


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

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CEU Credits:  0.6 CEUs

Course Description

Artificial intelligence (AI) and machine learning (ML) are considered to be some 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-MIMO 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. For this, we will use Sionna, an open-source software library for GPU-accelerated link-level simulations and 6G research with native support for the integration of neural networks. The attendees will receive detailed Jupyter notebooks with code examples to deepen their understanding and to quickly explore their own research ideas.

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


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


Sebastian Cammerer

Sebastian Cammerer

Research Scientist


Fayçal Aït Aoudia

Fayçal Aït Aoudia

Research Scientist


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.
  • Setup simple experiments to solve physical layer problems using neural networks.
  • Understand the idea of end-to-end learning and related challenges.
  • Recognize the most important state-of-the-art neural network architectures relevant to the physical layer.

Course Content

The following topics will be covered:

  • Part I - Introduction & Primer on Machine Learning and Deep Learning
    • Introduction & Machine Learning Basics
      • 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
      • Feed-forward neural networks
      • Gradient descent & Backpropagation
    • Advanced Neural Network Architectures
      • Convolutional neural networks
        • Stride and pooling
        • Receptive field and dilated convolutions
        • Depth-wise separable convolutions
      • Skip connections & ResNets
      • Attention & Transformers
      • Graph Neural Networks
    • Introduction to the Sionna Software Library
  • Part II - Applications of Deep Learning for Communications
    • Neural OFDM multi-user MIMO receivers
      • Hands-on: Implementation of a neural single-user OFDM receiver
      • Extension to multi-user OFDM MIMO
      • Comparison of recent deep learning-based MIMO receivers
    • Deep Learning for Channel Decoding
      • Weighted belief propagation decoding
      • Learned message passing decoding using graph neural networks
    • End-to-end Learning
      • What is E2E learning?
      • Bit-Metric Decoding Rate
      • Geometric shaping
      • Gradient estimation
      • Hands-on: Train your first Autoencoder
      • Turbo Autoencoders
  • Outlook and Other Topics

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, Educational Services Sr Program Manager.

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