AI/ML in 5G Challenge

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.

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.

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.

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.

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.

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.

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

First of use cases in 2030?

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.