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​​​​​​​​​​​​​​The Grand Challenge Finale Schedule​

Online, 15-17 December 2020 


Note: Registration for Tues 15 Dec and Wed. 16 Dec is different from Thurs. 17 Dec

Day 1​​ - ​Tuesday, 1​5 December 2020 (Time zone - CET)​​

Watch Video Recording (15 December event)

​​Join session to test connection
Opening Ceremony

  • Welcome remarks Reinhard Scholl​, Deputy Director, Telecommunication Standardization Bureau, ITU
Problem Statement –  5G+AI+AR 
  • PS-001.1  5Gn View remote collaboration Project [Presentation​[GitHub Repo]
    5Gfan​:  ​Jiawang Liu and Jiaping Jiang​, CITC of China Unicom, China
Problem Statement –  Analysis on route information failure in IP core networks by NFV-based test environment (KDDI, Japan).
  • PS-032.2  Analysis on route information failure in IP core networks by NFV-based test environment  [Presentation[GitHub Repo​]
    UT-NakaoLab-AI:  ​Fei Xia, Aerman Tuerxun, Jiaxing Lu, and Ping Du​, University of Tokyo​, Japan

  • PS-032.1  On Failure Classification Based on GNN in IP Core Networks by NFV-Based Test Environment​  [Presentation[GitHub Repo]
    naist-lsmI:  ​Takanori Hara, and Kentaro Fujita, Nara Institute of Science and Technology, Japan

  • PS-032.3  Pre-training and fine-tuning approach for detecting route information failures in IP core networks  [Presentation​[GitHub Repo]
    mlab:  ​Ryoma Kondo, Takashi Ubukata, Kentaro Matsuura, and Hirofumi Ohzeki​, University of Tokyo​, Japan
13:15-13:30Problem Statement –  Fault Localization of Loop Network Devices based on MEC Platform  
  • PS-002.1  Fault Localization of Loop Network Devices based on MEC Platform  [Presentation​[GitHub Repo]
    国创矩阵 (GuoChuang ChapterIX)​:  ​Zhang Qi and Lin Xueqin​​, GUOCHUANG SOFTWARE CO., LTD., China
Problem Statement –  Network topology optimization​ 
  • ​PS-007.1  AI-Based Network Topology Optimization  [Presentation​[Report​]
    小智 (Weeny Wit)​:  ​Han Zengfu, Wang Zhiguo, Zhang Yiwei, Wu Desheng, Li Sicong​, China Mobile Shandong, China
  • PS-007.2  Applying Machine Learning in Network Topology Optimization​​  [Presentation[Report]
    No Boundaries​:  ​Gang Zhouwei, Rao Qianyin, Feng Zezhong, Xi Lin, Guo Lin​​, China Mobile Guizhou, China​
Coffee break
14:15-14:30​ Problem Statement –  Energy-Saving Prediction of Base Station Cells in Mobile Communication Network  
  • PS-005.1  Presentation Title  [Presentation] [GitHub Repo]
    ​浑水摸鱼 (Cresting):  ​Wei Jiang, Shiyi Zhu, Xu Xu​​, AsiaInfo Technologies Limited​, China
Problem Statement –  Out of Service(OOS) Alarm Prediction of 4/5G Network Base Station 
  • PS-008.1  Out of Service(OOS) Alarm Prediction  [Presentation​[Report​]
    ​黑白双煞 (NKU-Excavator):  ​Zhou Chao, Zheng Tianyu, Jiang Meijun​​, Nankai university​, China​

Problem Statement –  Demonstration of MLFO capabilities via reference implementations 
  • PS-024.1  MLFO Demonstration using Reference Implementation  [Presentation​] [Git​Hub Repo]
    Abhishek Dandekar​​, TU Berlin​​, Germany

Problem Statement –  ML5G-PHY -Beam-Selection: Machine Learning Applied to the Physical Layer of Millimeter-Wave MIMO Systems (UFPA, Brazil)
  • PS-012.2  NN-based mmWave Beam Selection utilizing LIDAR Data​  [Presentation[GitHub Repo]
    Imperial_IPC1:  ​Mahdi Boloursaz Mashhadi, Tze-Yang Tung, Mikolaj Jankowski, Szymon Kobus​, Imperial College London​, United Kingdom

  • PS-012.3  Deep Learning on Multimodal Sensor Data for Fast mmWave Beam Selection​  [Presentation[GitHub Repo​]
    ​NU Huskies:  ​Batool Salehihikouei, Debashri Roy, Guillem Reus Muns, Zifeng Wang, Tong Jian​, Northeastern University, US

  • PS-012.1  AI-Aided mmWave Beam Selection for Vehicular Comm​unication  [Presentation[GitHub Repo]
    ​Bea​mSoup:  ​Zecchin Matteo, Eurecom​​​, France

Problem Statement –  Improving the capacity of IEEE 802.11 WLANs through Machine Learning (UPF, Spain)
  • PS-013.2  Multi-Layer Perceptron for OBSS throughput prediction  [Presentation[GitHub Repo]
    Ramon Vallès, Universitat Pompeu Fabra​​​, Spain

  • PS-013.3  A Graph Neural Network approach for throughput prediction in next-generation WLANs  [Presentation[GitHub Repo​]
    ​ATARI​:  Paola Soto, David Goez, Miguel Camelo, Natalia Gaviria, University of Antwerp (Belgium) and  Universidad de Antioquia (Colombia)

  • PS-013.1  Dynamic Channel Bonding with Machine Learning  [Presentation[GitHub Repo​]
    ​STC_2:  ​Mohammad Abid, Ayman M. Aloshan, Faisal Alomar, Mohammad Alfaifi, Abdulrahman Algunayyah, Khaled M. Sahari​, Saudi Telecom Company​​​, Saudi Arabia

Day 2​​ - ​Wednesday​, 16 December 2020 (Time zone - CET)​​

Watch Video Recording (16 December event)

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Problem Statement –  5G+AI (Smart Transportation) - (JNU, India) 
  • PS-019.1  Automated defect identification by AI video/image analytics with 5G- enabled remote road fixing vehicle​  [Presen​tation[GitHub Repo]
    ​STC:  ​Atheer K. Alsaif, Nora M. Almuhanna, Abdulrahman Alromaih, Abdullah O. Alwashmi, Saudi Telecom Company, Saudi Arabia

Problem Statement –  Network State Estimation by Analyz-ing Raw Video Data (NEC, Japan)
  • PS-031.1  Challenge for estimation of bandwidth and loss rate by focusing on the degradation characteristics of raw video data  [Presentation​[GitHub Repo]
    ​JOJO: Yuusuke Hashimoto, Yuya Seki, Daishi Kondo, Osaka Prefecture University​​​, Japan

  • PS-031.2  A Lightweight deep learning model for network state estimation using raw video data’  [Presentation[GitHub Repo]
    KCGI​:  ​Yimeng Sun, Badr Mochizuki, Kyoto College of Graduate Studies for Informatics, Japan

  • PS-031.3  Network State Estimation by Analyzing Raw Video Data  [Presentation​[GitHub Repo]
    ​HENOKO KING:  ​Fuyuki Higa, Gen Utidomari, Ryuma Kinjyo, Nao Uehara​​, National Institute of Technology, Okinawa College, Japan
13:00-13:30​ Problem Statement –  Compression of Deep Learning models (ZTE)
  • PS-018.1  End-Edge Cooperative Inference of Deep Learning Model Based on DNN Partition  [Presentation​[GitHub Repo]
    ​AI-Maglev: Yuwei Wang, Sheng Sun​, Institute of Computing Technology Chinese Academy of Sciences, China

  • PS-018.2  Multi-Context based Knowledge Distillation  [Presentation[GitHub Repo]
    GAN Torrents​: ​Satheesh Kumar Perepu,Saravanan Mohan, Vidya G, Thrivikram G L, Sethuraman T V, Ericsson Research India​
13:30-13:45​ Problem Statement –  Privacy Preserving AI/ML in 5G net-works for healthcare applications (C-DOT, India)
  • PS-022.1  Dopamine: Differentially Private Secure Federated Learning on Medical Data  [​Pres​entation[GitHub Repo]
    ​I****​**L diagnostics:  ​Mohammad Malekzadeh, Mehmet Emre Ozfatura,Kunal Katarya, ​Mital Nitish, Burak Hasircioglu​, ​ Imperial College London, United Kingdom
Problem Statement –  Shared Experience Using 5G+AI (3D Augmented + Virtual Reality)​ - (Hike, India) 
  • PS-023.1  Shared Experience Using 5G+AI (3D Augmented + Virtual Reality)  [Presentation] [GitHub Repo]
    Nitish ​Kumar Singh​​​, Easyrewardz India, India​
Coffee break

Problem Statement –  Graph Neural Networking Challenge 2020 (BNN-UPC, Spain)
  • PS-014.1  Graph Neural Networks for Physical Networks Modeling  [Presentation[GitHub Repo​]
    ​Steredeg: Loïck Bonniot, Christoph Neumann, François Schnitzler, François Taiani, InterDigital; Inria/Irisa

  • PS-014.3  Hyperparameter Tuning for the RouteNet Modela  [Presentation[GitHub Repo​]
    Gradient Ascent​:  ​Nick Vincent Hainke, Stefan Venz, Johannes Wegener, Henrike Wissing,  Fraunhofer HHI, Germany

  • PS-014.2  A RouteNet Modification for Estimating Delays in Networks with Scheduling  [Presentation[GitHub Repo]
    ​​Salzburg Research:  ​​Martin Happ, Christian Maier, Jia Lei Du, Matthias Herlich,  Salzburg Research Forschungsgesellschaft mbH​
Problem Statement –  Using weather info for radio link failure (RLF) prediction (Turkcell, Turkey)
  • PS-036.1  Radio Link Failure Prediction  [Presentation[GitHub Repo]
    ​Link Busters: Dheeraj Kotagiri, Anan Sawabe , Takanora Iwai,  NEC Corporation, Japan

  • PS-036.2  Radio Link Failure (RLF) Prediction using Weather Information  [Presentation[GitHub Repo]
    IEC_Research:  ​Juan Samuel Pérez, Amín Deschamps, Willmer Quiñones, Yobany Díaz​, INTEC University, Dominican Republic
Problem Statement –  Traffic recognition and Long-term traf-fic forecasting based on AI algorithms and metadata for  5G/IMT-2020 and beyond (SPbSUT)​ 
  • PS-038.1  Traffic recognition and long-term traffic forecasting based on AI algorithms and metadata  [Presentation​[GitHub Repo]
    USATU​: Ainaz Hamidulin, Viktor  Adadurov, Denis Garaev, Artem Andriesvky​​​, USATU University, Russia

Problem Statement –  ML5G-PHY- Channel Estimation @NCSU: Machine Learning Applied to the Physical Layer of Millimeter-Wave MIMO Systems at North Carolina State University (NCSU, US)
  • PS-025.1  Learning to detect: on site-specific channel estimation with hybrid MIMO architectures  [Presentation[GitHub Repo]
    ML-DOJO: Dolores Garcia, Joan Palacios, Joerg Widmer, IMDEA Networks, Spain

  • PS-025.2  Sparse Bayesian Learning for Site-Specific Hybrid MIMO Channel Estimation  [Presentation​[GitHub Repo]
    ICARUS​:  ​Emil Björnson, Pontus Giselsson, Mustafa Cenk Yetis, Özlem Tugfe Demir,  Linköping University and Lund University, Sweden

  • PS-025.3  A Multilevel-Greedy and Bayesian Compressive Channel Estimator for Frequency-Selective Hybrid mmWave MIMO Systems  [Presentation​[GitHub Repo]
    ​​Learned Chester​:  Chandra Murthy, Christo Kurisummoottil Thomas, Marios Kountouris, Rakesh Mundlamuri,  Sai Subramanyam Thoota, Sameera Bharadwaja H,  Eurecom, France, and Indian Institute of Science, India​

Day 3​​ -
Thursday​, 17 December 2020 (Time zone - CET)​​

Watch Video Recording (17 December event)

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Opening Ceremony​​

  • ​Welcome remarks: Houlin Zhao, Secretary General​, ITU 
  • Welcome remarksTSB Dir Chaesub Lee, Director, Telecommunication Standardization Bureau, ITU
  • Welcome remarksH.E. Eng. Majed Sultan Al Mesmar, Deputy Director General Telecommunication Regulatory Authority​
  • ​​​ITU AI/ML in 5G Challenge - The Journey​ Slides​: Thomas B​asikolo, ITU 
12:30-12:​55​ Keynote​ – Recent advances in Federated Learning for Communication

Speaker: Wojciech Samek, Fraunhofer HHI

​Special Session: Vision for the future - AI/ML in 5G roadmap

  • ​Regulator PerspectiveTRA-UAE 
  • Industry Perspective: Bob Everson, Senior Director, 5G Architecture Cisco​​ ​​
  • ​​​Industry Perspective: Wei Meng, Director of Standard and Open Source Planning, ZTE Corporation ​
Keynote: The Unfinished Journey of Network AI

SpeakerDr. Chih-Lin I (Chief Scientist, Wireless Technologies, China Mobile Research Institute) 

Keynote: Learning at the Wireless Edge

SpeakerH. Vincent Poor - Professor of Electrical Engineering  - Princeton University 


Final Presentation: ​Overview of the solutions from the Challenge from 3 representative Teams

Award announcements: ​prizes and certificates ​

Call for papers: Special issue of ITU Journal on Future and Evolving Technologies (ITU J-FET)​): “AI/ML Solutions in 5G and Future Networks”

SpeakerAlessia Magliarditi,  ITU <S​lides​>


2021 outlook for Challenge 2.0 Slides​

Speaker: Vishnu Ram

Closing Ceremony​​

  • Closing remarksRemarks from some hosts of the 2020 Challenge
  • TSB Dir Chaesub Lee, Director, Telecommunication Standardization Bureau, ITU