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AI/ML in 5G Challenge 2022

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.

Register for the Challenge!

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.

Register for the Challenge!
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.

CHALLENGE GUIDELINES
GPU REQUEST FORM
CHALLENGE GUIDELINES
GPU REQUEST FORM

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)

LEARN MORE

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)

LEARN MORE

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)

LEARN MORE

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)

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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)

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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)

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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)

LEARN MORE

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)

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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

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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)

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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)

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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)

LEARN MORE

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)

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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 Focus Group ML in 5G
ITU J-FET
AI/ML in 5G Challenge 2021
AI/ML in 5G Challenge 2020
ITU Focus Group ML in 5G
ITU J-FET
AI/ML in 5G Challenge 2021
AI/ML in 5G Challenge 2020

ITU AI/ML 5G Challenge Playlist

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View full playlist
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AI for Good ML/5G partners

Sponsors

MSIT Korea
MSIT Korea
ZTE
ZTE

Hosts of problem statements

Arizona State University
Arizona State University
Barcelona Neural Net-working Center (BNN-UPC)
Barcelona Neural Net-working Center (BNN-UPC)
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Universitat Pompeu Fabra (UPF)
Universitat Pompeu Fabra (UPF)
LF Networking
LF Networking
ZTE
ZTE
China Mobile
China Mobile
RISING
RISING
NIST (National Institute of Standards and Technology)
NIST (National Institute of Standards and Technology)
KDDI Research, Inc.
KDDI Research, Inc.
ITU Focus Group on Autonomous Networks (FG-AN)
ITU Focus Group on Autonomous Networks (FG-AN)
Northeastern University
Northeastern University

Technical partner

Jarvis Labs
Jarvis Labs
Artificial Intelligence Industry Alliance
Artificial Intelligence Industry Alliance

Sponsorship Inquiries​


    Testimonials

    The ITU AI/ML in 5G Challenge has provided a great opportunity to bring together standardization activities with academia by allowing students and professionals to solve important problems in communications. In particular, the challenge has led to obtaining very interesting results that may open the door to revolutionize the way communications are understood. For the Dynamic Channel Bonding problem, the application of Deep Learning models accounts for a significant breakthrough in the field.

    Francesc Wilhelmi, PhD Student
    Wireless Networking Research Group, UPF, Spain

    We are grateful to the “ITU AI/ML in 5G Challenge” for giving us the opportunity to connect with AI researchers around the globe in solving an operational challenge for Turkcell. Our challenge topic, “Radio link failures”, has critical implications for an operator network. Modelling link failures based on weather forecasts is a daunting task due to the unreliable nature of forecasts and rare occurrences of radio failures. Challenge preparation and communication with contestants have already helped us better formulate the problem, resolve inconsistences in the data, and consider alternative approaches. We are expecting to receive highquality solution proposals for this challenging task and we are looking forward to applying the learnings from this challenge to implement mechanisms that take precautions before radio link failures occur, to improve customer experience.

    Salih Ergut
    5G R&D Senior Expert, Turkcell, Turkey

    The main important task for AI in 5G is traffic recognition “on the fly” without making delays for new services traffic management such as the Tactile Internet, medical networks, and autonomous vehicles.

    Artem Volkov, Researcher and PhD Student
    Telecommunication Networks and Data Transmission Dept., SPbSUT, Russia

    Hosting a competition within the AI/ML in 5G Challenge has been a completely worthwhile experience. ITU organizers have made it very easy to prepare and run the whole competition, and have done a great job in attracting participants. In our problem statement, the participation numbers were far beyond what we could expect at the beginning. As members of academia, we see this Challenge as a pathway to disseminate the research we do. This kind of competition is very interesting for us to connect with a different public from both the academia and industry. Moreover, we were impressed by the proposed solutions, some of them pushing the state of the art. We are looking forward to the next edition in 2021.

    José Suárez-Varela, PostDoctoral Researcher,
    BNN-UPC, Spain

    We are very honoured to be a part of the ITU AI/ ML in 5G Challenge — a great opportunity to accelerate the pace of applying AI/ML algorithms in telecommunication networks. We proposed a problem statement “DNN model inference optimization” in the Enabler Track. The topic is quite important when considering deploying ML models in the network, especially where there are strict requirements for inference performance. We have received some great submissions so far, which we believe will definitely enlighten our work in the Adlik open source project.

    Liya Yuan, Open Source and Standardization Engineer
    ZTE, China

    The Challenge provides a stage to showcase the potential of applying machine learning to enable network intelligence. It attracts more researchers and engineers to put effort into the research and development of the intelligent network and has gathered the operators, suppliers, researchers, college students and related communities together to build a more flexible, efficient, green, and resilient network. By integrating AI into the network services, the network is being evolved to support better social, commercial and technology services. The Artificial Intelligence Industry Alliance will continuously support ITU in organizing challenges to drive the convergence of AI technologies and network infrastructure and services.

    Qiang Cheng
    Artificial Intelligence Industry Alliance (AIIA), China

    To ensure network coverage density even in sparsely populated areas while guaranteeing high security and satisfying quality of service, an integration of intelligence edge computing and blockchain technology will play an important role in future 2030 networks

    Ammar Muthanna, Deputy Head in Science
    Telecommunication Networks and Data Transmission Dept., SPbSUT, and Head, SDN Laboratory, Russia

    First of use cases in 2030?

    Andrey Koucheryavy
    Chair and Professor of Telecommunication Networks and Data Transmission Dept., SPbSUT, Chief Researcher NIIR, Russia, and Chairman of ITU–T SG11

    The ITU AI/ML in 5G Challenge helps to depart from the “smalldata regime” and properly assess AI/ML algorithms such as deep learning, with large datasets and reproducible experiments.

    Aldebaro Klautau Professor of Electrical and Computer Engineering
    UFPA, Brazil

    We are very honored to be part of this very exciting ITU AI/ML in 5G Challenge and to provide a problem set. AI/ML adoption is still at the very early stage in the telecommunication industry and innovation will definitely be brought by very unique ITU communities consisting of different countries, organizations, and backgrounds. Let’s keep innovative and collaborative in the 5G era and beyond.

    Tomohiro Otani, Executive Director
    KDDI Research, Japan

    We posit that one of the promising research and development directions for Beyond 5G/6G telecommunications is enabling “autonomic intelligence” in networking, as outlined by the “Beyond 5G Strategic Board” held by the Japanese Government’s Ministry of Internal Affairs and Communications. We observe that the ITU AI/ML in 5G Challenge is in perfect alignment with the strategy and has been quickly and globally encouraging young researchers to participate in competitions. It is an indication that ITU–T’s activity is proving to be an effective means for putting forth the strategic direction of R&D for enabling superintelligence in networking. In Japan, RISING (Cross-Field Research Association of SuperIntelligent Networking) has led the regional competition of ITU’s AI/ML in 5G Challenge, fully supported by MIC, 5GMF, TTC, and industry partners, KDDI and NEC. I really appreciate ITU’s global promotion of applying AI/ML in telecommunications and I am looking forward to a successful outcome of the activities.

    Akihiro Nakao, Professor,
    UTokyo, Japan

    The Ministry of Internal Affairs and Communications in Japan recognizes that research and development on network autonomy using AI/ML is extremely important for the further development of 5G and the early realization of B5G (Beyond 5G). By using AI/ML and competing to solve various 5G network problems, engineers will greatly contribute to the development of information and communications, such as the improvement of AI/ML technology and human resource development. The global ITU AI/ML in 5G Challenge is a very meaningful activity.

    Ministry of Internal Affairs and Communications (MIC)
    Japan

    Running the ITU challenge on MIMO channel estimation at millimeter wave has been a great way to kick off my new research program at North Carolina State University. It has made me realize that putting together the AI and wireless communities, so they can speak the same language, is a huge challenge. I have enjoyed exposing the participants to one of my favourite problems in millimeter wave MIMO communications.

    Nuria Gonzalez Prelcic, Associate Professor,
    NCSU, United States

    My participation in the ITU AI/ ML in 5G Challenge has allowed me to gain hands-on experience of relevant topics to build and design future wireless technologies. Specifically, it has been exciting to apply different ML methods to the particularities of future spectrum access mechanisms that allow even better performance of what we have today.

    Paola Soto-Arenas, PhD Researcher
    University of Antwerp, Belgium

    Participating in this challenge was a golden chance to test and improve our ML skills in the context of newly introduced technology such as 5G. During this competition, we have managed to overcome some challenges and meet new people from around the world.

    Khalid Al-Bagami, Telecom Engineer
    Ericsson, Saudi Arabia

    The challenge has given us the opportunity to rethink classical channel estimation approaches from an ML perspective. I am excited to see the results of other participants.

    Dolores Garcia, PhD Student
    IMDEA, Spain

    At UC3M we trained an NN solution to forecast 802.11 WLANs throughput. The organizers (UPF) provided both datasets, and support during the challenge. It has been a very fulfilling experience.

    Jorge Martín Pérez, PhD Student,
    UC3M, Spain

    I’m a health tech enthusiast and my UNDP R42 group’s work is on an AI in 5G mobile chatbot providing COVID-19 information. My work spans Africa and Europe, and I aim to reach more regions to influence global markets and society, young and old. ITU has been fantastic in bringing together such dynamic participants to take part in innovations for 2020 challenges.

    Mahlet Shimellis, UNDP
    Ethiopia/United States

    I feel ecstatic to say that my participation in the ITU AI/ ML in 5G Challenge has been a great learning experience. It’s been a good opportunity to work on the exploratory problems in wireless communication and trying to find solutions to them using AI/ ML approaches.

    Megha Gururaj Kulkarni, student
    PES University, India

    The ITU AI/ML in 5G Challenge has provided a great opportunity to bring together standardization activities with academia by allowing students and professionals to solve important problems in communications. In particular, the challenge has led to obtaining very interesting results that may open the door to revolutionize the way communications are understood. For the Dynamic Channel Bonding problem, the application of Deep Learning models accounts for a significant breakthrough in the field.

    Francesc Wilhelmi, PhD Student
    Wireless Networking Research Group, UPF, Spain

    We are grateful to the “ITU AI/ML in 5G Challenge” for giving us the opportunity to connect with AI researchers around the globe in solving an operational challenge for Turkcell. Our challenge topic, “Radio link failures”, has critical implications for an operator network. Modelling link failures based on weather forecasts is a daunting task due to the unreliable nature of forecasts and rare occurrences of radio failures. Challenge preparation and communication with contestants have already helped us better formulate the problem, resolve inconsistences in the data, and consider alternative approaches. We are expecting to receive highquality solution proposals for this challenging task and we are looking forward to applying the learnings from this challenge to implement mechanisms that take precautions before radio link failures occur, to improve customer experience.

    Salih Ergut
    5G R&D Senior Expert, Turkcell, Turkey

    The main important task for AI in 5G is traffic recognition “on the fly” without making delays for new services traffic management such as the Tactile Internet, medical networks, and autonomous vehicles.

    Artem Volkov, Researcher and PhD Student
    Telecommunication Networks and Data Transmission Dept., SPbSUT, Russia

    Hosting a competition within the AI/ML in 5G Challenge has been a completely worthwhile experience. ITU organizers have made it very easy to prepare and run the whole competition, and have done a great job in attracting participants. In our problem statement, the participation numbers were far beyond what we could expect at the beginning. As members of academia, we see this Challenge as a pathway to disseminate the research we do. This kind of competition is very interesting for us to connect with a different public from both the academia and industry. Moreover, we were impressed by the proposed solutions, some of them pushing the state of the art. We are looking forward to the next edition in 2021.

    José Suárez-Varela, PostDoctoral Researcher,
    BNN-UPC, Spain

    We are very honoured to be a part of the ITU AI/ ML in 5G Challenge — a great opportunity to accelerate the pace of applying AI/ML algorithms in telecommunication networks. We proposed a problem statement “DNN model inference optimization” in the Enabler Track. The topic is quite important when considering deploying ML models in the network, especially where there are strict requirements for inference performance. We have received some great submissions so far, which we believe will definitely enlighten our work in the Adlik open source project.

    Liya Yuan, Open Source and Standardization Engineer
    ZTE, China

    The Challenge provides a stage to showcase the potential of applying machine learning to enable network intelligence. It attracts more researchers and engineers to put effort into the research and development of the intelligent network and has gathered the operators, suppliers, researchers, college students and related communities together to build a more flexible, efficient, green, and resilient network. By integrating AI into the network services, the network is being evolved to support better social, commercial and technology services. The Artificial Intelligence Industry Alliance will continuously support ITU in organizing challenges to drive the convergence of AI technologies and network infrastructure and services.

    Qiang Cheng
    Artificial Intelligence Industry Alliance (AIIA), China

    To ensure network coverage density even in sparsely populated areas while guaranteeing high security and satisfying quality of service, an integration of intelligence edge computing and blockchain technology will play an important role in future 2030 networks

    Ammar Muthanna, Deputy Head in Science
    Telecommunication Networks and Data Transmission Dept., SPbSUT, and Head, SDN Laboratory, Russia

    First of use cases in 2030?

    Andrey Koucheryavy
    Chair and Professor of Telecommunication Networks and Data Transmission Dept., SPbSUT, Chief Researcher NIIR, Russia, and Chairman of ITU–T SG11

    The ITU AI/ML in 5G Challenge helps to depart from the “smalldata regime” and properly assess AI/ML algorithms such as deep learning, with large datasets and reproducible experiments.

    Aldebaro Klautau Professor of Electrical and Computer Engineering
    UFPA, Brazil

    We are very honored to be part of this very exciting ITU AI/ML in 5G Challenge and to provide a problem set. AI/ML adoption is still at the very early stage in the telecommunication industry and innovation will definitely be brought by very unique ITU communities consisting of different countries, organizations, and backgrounds. Let’s keep innovative and collaborative in the 5G era and beyond.

    Tomohiro Otani, Executive Director
    KDDI Research, Japan

    We posit that one of the promising research and development directions for Beyond 5G/6G telecommunications is enabling “autonomic intelligence” in networking, as outlined by the “Beyond 5G Strategic Board” held by the Japanese Government’s Ministry of Internal Affairs and Communications. We observe that the ITU AI/ML in 5G Challenge is in perfect alignment with the strategy and has been quickly and globally encouraging young researchers to participate in competitions. It is an indication that ITU–T’s activity is proving to be an effective means for putting forth the strategic direction of R&D for enabling superintelligence in networking. In Japan, RISING (Cross-Field Research Association of SuperIntelligent Networking) has led the regional competition of ITU’s AI/ML in 5G Challenge, fully supported by MIC, 5GMF, TTC, and industry partners, KDDI and NEC. I really appreciate ITU’s global promotion of applying AI/ML in telecommunications and I am looking forward to a successful outcome of the activities.

    Akihiro Nakao, Professor,
    UTokyo, Japan

    The Ministry of Internal Affairs and Communications in Japan recognizes that research and development on network autonomy using AI/ML is extremely important for the further development of 5G and the early realization of B5G (Beyond 5G). By using AI/ML and competing to solve various 5G network problems, engineers will greatly contribute to the development of information and communications, such as the improvement of AI/ML technology and human resource development. The global ITU AI/ML in 5G Challenge is a very meaningful activity.

    Ministry of Internal Affairs and Communications (MIC)
    Japan

    Running the ITU challenge on MIMO channel estimation at millimeter wave has been a great way to kick off my new research program at North Carolina State University. It has made me realize that putting together the AI and wireless communities, so they can speak the same language, is a huge challenge. I have enjoyed exposing the participants to one of my favourite problems in millimeter wave MIMO communications.

    Nuria Gonzalez Prelcic, Associate Professor,
    NCSU, United States

    My participation in the ITU AI/ ML in 5G Challenge has allowed me to gain hands-on experience of relevant topics to build and design future wireless technologies. Specifically, it has been exciting to apply different ML methods to the particularities of future spectrum access mechanisms that allow even better performance of what we have today.

    Paola Soto-Arenas, PhD Researcher
    University of Antwerp, Belgium

    Participating in this challenge was a golden chance to test and improve our ML skills in the context of newly introduced technology such as 5G. During this competition, we have managed to overcome some challenges and meet new people from around the world.

    Khalid Al-Bagami, Telecom Engineer
    Ericsson, Saudi Arabia

    The challenge has given us the opportunity to rethink classical channel estimation approaches from an ML perspective. I am excited to see the results of other participants.

    Dolores Garcia, PhD Student
    IMDEA, Spain

    At UC3M we trained an NN solution to forecast 802.11 WLANs throughput. The organizers (UPF) provided both datasets, and support during the challenge. It has been a very fulfilling experience.

    Jorge Martín Pérez, PhD Student,
    UC3M, Spain

    I’m a health tech enthusiast and my UNDP R42 group’s work is on an AI in 5G mobile chatbot providing COVID-19 information. My work spans Africa and Europe, and I aim to reach more regions to influence global markets and society, young and old. ITU has been fantastic in bringing together such dynamic participants to take part in innovations for 2020 challenges.

    Mahlet Shimellis, UNDP
    Ethiopia/United States

    I feel ecstatic to say that my participation in the ITU AI/ ML in 5G Challenge has been a great learning experience. It’s been a good opportunity to work on the exploratory problems in wireless communication and trying to find solutions to them using AI/ ML approaches.

    Megha Gururaj Kulkarni, student
    PES University, India

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