AI/ML in 5G Challenge

Applying machine learning in communications networks.

Join the ITU AI/ML in 5G Challenge in 2022, a platform to apply machine learning in communication networks. Last year’s edition connected more than 1600 students and professionals from 82 countries.

The Challenge offers carefully curated problem statements, a mix of real-world and simulated data, technical webinars, mentoring, and hands-on sessions. Teams participating in the Challenge enable, create, train and deploy ML models for communication networks. This enables participants to not only showcase their talent, test their concepts on real data and real-world problems, and compete for global recognition including prize money and certificates, but also enter the world of ITU standards by mapping their solutions to our specifications.

Join the ITU AI/ML in 5G Challenge in 2022, a platform to apply machine learning in communication networks. Last year’s edition connected more than 1600 students and professionals from 82 countries.

The Challenge offers carefully curated problem statements, a mix of real-world and simulated data, technical webinars, mentoring, and hands-on sessions. Teams participating in the Challenge enable, create, train and deploy ML models for communication networks. This enables participants to not only showcase their talent, test their concepts on real data and real-world problems, and compete for global recognition including prize money and certificates, but also enter the world of ITU standards by mapping their solutions to our specifications.

Compute platform

ITU provides a state-of-the-art, free-of-charge compute platform to participants of the Challenge who do not have adequate access to compute in their respective institutions. The compute platform will provide participants with access to:

  • Free GPUs and CPUs
  • Hosted Jupyter notebook server
  • Python kernel
  • Pre-installed machine learning packages, e.g. PyTorch and Tensorflow

In some of the problem statements, a baseline or reference solution may be offered which may include implementations using Jupyter notebooks.

ML/5G Challenge problem statements

Federated Traffic Prediction

Usage of federated learning tools to predict traffic in cellular networks from real measurements

Curated by CTTC (Centre Tecnològic de Telecomunicacions de Catalunya)

Graph Neural Networking Challenge

Exploring a data-centric approach to develop Network Digital Twins by producing a training dataset that results in better performance of the target GNN model

Curated by BNN-UPC (Barcelona Neural Networking Center)

Beam Classification with DeepBeam

To create a classifier for transmit beams out of a codebook using neural networks with the DeepBeam Dataset

Curated by Northeastern University (USA)

Depth Map Estimation in 6G

Propose an AI/ML algorithm that reconstructs the depth map of an indoor environment from mmWave MIMO channel impulse responses and depth map representation

Curated by NIST (National Institute of Standards and Technology)

Network failure prediction

Create AI/ML models for predicting network failures using time-series data consisting of thousands of metrics in 5G core network

Curated by KDDI (Japan)

Location Estimation Using RSSI

To develop an AI/ML-based localization algorithm/technique that accurately estimate the position of a receiver based on RSS information

Curated by RISING (Japan)

Behavioral modeling for energy efficiency

To bring AI algorithms into behavioral modeling of power amplifiers, helpful for developing highly efficient 5G wireless communication systems

Curated by ZTE (China)

Throughput Prediction in Wi-Fi networks

Use of machine learning to predict achievable throughput that a subset of APs transmitting at the same time can achieve

Curated by UPF (Universitat Pompeu Fabra)

Data Generation using GANs

Generating synthetic observability data using GANs for Telco-Cloud Infrastructure Metrics to solve the problem of dataset unavailability for AI/ML researchers

Curated by LF Networking

Build your own Closed loop

Collaboratively create a crowdsourced, baseline representation for AN closed loops (controllers) as a proof of concept

Curated by FG-AN (ITU Focus Group Autonomous Networks)

Beam Prediction Challenge

To design machine learning-based models that can adapt to and perform accurate sensing-aided beam prediction at an entirely new location

Curated by Arizona State University (USA)

Classification of network users

To classify each user and accurately distinguish between users with bad experience and users with good experience using ML/DL methods

Curated by ZTE (China)

Slidin’ videos

Create the best AI model which annotates slide transitions in video and extract titles of each slide

Curated by ITU (International Telecommunication Union)

ITU AI/ML 5G Challenge Timeline

February – May 2022

June – October 2022

November – December 2022

Curation Phase

Competition Phase

Evaluation Phase

Registration open until 21 October 2022

Submission deadline 28 October 2022

Evaluation of solutions: October 2022

Final Ranking: 31 October 2022

Preparation of reports: October – November 2022

November 2022 – Judges Panel evaluates the best solutions from Competition Phase

29 Nov – 01 Dec 2022 – Best solutions pitch in a 3-day event end of to determine the finalists

14 December 2022 – Grand Challenge Finale

ITU AI/ML 5G Challenge Timeline

February – May 2022

Curation Phase

June – October 2022

Competition Phase

  • 21 October 2022 – Registration
  • 28 October – Submission of solutions
  • October 2022 – Evaluation of solutions
  • October – November 2022 – Preparation of reports

November – December 2022

Evaluation Phase

  • November 2022 – Judges Panel evaluates the best solutions from Competition Phase
  • Best solutions pitch in a 3-day event 29 Nov – 01 Dec 2022 to determine the finalists
  • 14 December 2022 – Grand Challenge Finale

ITU AI/ML 5G Challenge Playlist

Related sessions
15 July 2022
14:00 - 15:10 CEST, Geneva | 08:00-09:10 EDT, New York | 20:00-21:10 CST, Beijing
Yansha Deng (King's College London), Miya Nishio (University of Tokyo), Vishnu Ram OV (International Telecommunication Union (ITU))
27 July 2022
16:00 - 17:30 CEST, Geneva | 10:00-11:30 EDT, New York | 22:00-23:30 CST, Beijing
Kirill Ekshembeev, Miya Nishio (University of Tokyo), Vishnu Ram OV (International Telecommunication Union (ITU))...
24 August 2022
09:00 - 10:30 CEST, Geneva | 16:00-17:30 JST, Tokyo
Koichi Adachi (University of ElectroCommunications), Norihiro Fukumoto (University of Tokyo), Takanori Iwai (Secure System Platform Research Laboratories)...
26 August 2022
16:00 - 17:00 Geneva | 10:00-11:00 EST, New York | 22:00-23:00 CST, Beijing
Miya Nishio (University of Tokyo), Rohit Singh Rathaur (BIT Ranchi)
AI for Good ML/5G partners

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