Coming Up: https://aiforgood.itu.int/event/itu-ai-ml-in-5g-grand-challenge-finale-2/
Contact Vishnu Ram: Vishnu Ram OV <email@example.com>
ITU-T Secretariat Contacts.
Federal University of Pará (UFPA), Brazil
beam-selection: Machine learning applied to the physical layer of millimeter-wave MIMO systems
Reinforcement learning: scheduling and resource allocation
Improving the capacity of IEEE 802.11 WLANs through machine learning
1.Finding groups of BSSs
2.Federated Learning for Spatial Reuse in WLANs
Graph Neural Networking (GNN) Challenge 2020
GNN Challenge. 2021: Creating a Scalable Network Digital Twin
North Carolina State University, US
channel estimation: Machine learning applied to the physical layer of millimeter-wave MIMO systems
Localization: Multidevice localization with mmWave signals in a factory environment
Network state estimation by analysing raw video data
Location estimation using RSSI of wireless LAN
Analysis on route information failure in IP core networks by NFV-based test environment
Network failure detection and root cause analysis in 5GC by NFV-based test environment
Using weather info for radio link failure (RLF) prediction
Radio Link Failure Prediction
St. Petersburg State Univ. of Telecom.
Traffic recognition and Long-term traffic forecasting based on AI algorithms and metadata
Forecasting Model for Service Allocation Network Using Traffic Recognition
1.Fault localization of loop network devices based on MEC platform
2.Energy-saving prediction of base station cells in mobile comm. network
3.Core network KPI index anomaly detection
4.Alarm and prevention for public health emergency based on telecom data
5.Configuration knowledge graph construction of loop network devices based on MEC architecture
Network anomaly detection based on logs
1.Network topology optimization
2.Out of service (OoS) alarm prediction of 4/5G network base station
Cross Layer user experience optimization – Radio link performance prediction
WALDO (Wireless Artificial intelligence Location DetectiOn): sensing using mmWave communications and ML
Hardware-Efficient Modulation Classification with RadioML
Univ. of Alabama
RF-Sensor Based Human Activity Recognition
Network resource allocation for emergency management based on Closed loop analysis
Delivery route optimization
The categories of the problems are as follows:
RAN: Radio Access N/W
PHY: Physical Layer
NFV: Network Functions Virtualization
WLAN: Wireless LAN(802.11)
TE: Traffic Engineering
AR: Artificial Reality
|Sl. No||Category of the Problems||Problems||Host||#of Competitors||Dataset|
ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN
|RISING Japan||38||The training data includes the location of AP, RSSI information within the coverage of the AP and its corresponding location. Link is not available.|
|2||NFV||Network failure detection and root cause analysis in 5GC by NFV-based test environment. Link||KDDI Japan||35||Did not find the public link.|
The data sets used for this challenge were created in the NFV-based test environment simulated for 5GC. In this sense, they are synthetic data, but as similar as the real data, resulting from our NFV-based test environment.
Federated Learning for Spatial Reuse in a multi-BSS (Basic Service Set) scenario Link
|UPF||22||A dataset generated with the Komondor simulator is provided to train ML models. Link|
Radio Link Failure Prediction Link
|Turkcell||35||Zip file contains the following tab separated files (tsv):|
• distances.tsv: pair-wise distances
• met-forecast.tsv: meteorology 5-day forecasts
• met-real.tsv: meteorology historic realizations
• met-stations.tsv: meteorology station information
• rl-kpis.tsv: radio link KPIs and configuration parameters
• rl-sites.tsv: radio link site information
Training Dataset: Link
Exemplary Colab Project by Turkcell: Link
Build-a-thon(PoC) Network resource allocation for emergency management based on closed-loop analysis Link
|ITU Focus Group on Autonomous - Networks||26|
Powerpoint slides for the progress of teams during the challenge
ITU-ML5G-PS-011: Combinatorial Optimization Challenge:- Delivery route optimization Link
The dataset is provided in the Dataset module.
Forecasting Model for Service Allocation Network Using Traffic Recognition Link
|The Bonch-Bruevich Saint Petersburg State University of Telecommunications||16||The training data in pcap format can be downloaded here.|
RF-Sensor Based Human Activity Recognition Link
|The University of Alabama||24|
Github with Data Download:
Watch the webinar here: https://aiforgood.itu.int/events/understanding-how-people-move-using-modern-civilian-radar/
ML5G-PHY-Localization: Multidevice localization with mmWave signals in a factory environment Link
|North Carolina State University||17|
The training data set consists of a collection of channels associated to the links between devices and access points in a factory environment and also their associated positions in the room. The factory environment has been simulated by ray tracing, with 12 access points (AP) located at the ceiling. The devices are randomly placed at the room and assigned to the AP with highest gain during the sector level sweep. For simplification, we assume perfect synchronization, this means that the first path arrives to the AP at time 0. The beam-training is measured. Then, with probabilities 50%, 25% and 25%, 0, 1 or 2 other devices interfere with the beam-training through random 4-QAM OFDM symbols using 512 frequency carriers.
Two datasets are provided, namely “train” and “test”. The dataset “train” consists on all the information required for the training of your algorithm, this is UE location and the AP it is associated to, while the dataset “test” lacks the UE localization.
Each participant can get access to each of the 12000 samples measurement through the provided executable file by passing as flags the name of the dataset and the indices [0-11999] of the samples to generate.
We also provide Python code to more easily understand the dataset through visualization.
The provided Python code generates the measurements regarding the sample indexes 0, 4 and 8, which happen to have 0, 1 and 2 interferers and then prints the information regarding these samples interferer and plots the power spectrums in time, direction of departure and direction of arrival.
These files can either be downloaded from the dataset tab once the team is registered or from the NCSU webpage of the problem.
ML5G-PHY-Reinforcement learning: scheduling and resource allocation Link
|UFPA||37||The dataset is composed of “traces” from simulations executed with Unreal Engine + AirSim, and will be available on June 30 at this folder|
WALDO (Wireless Artificial intelligence Location DetectiOn): sensing using mmWave communications and ML. Link
|National Institute of Standards and Technology||31|
For software and dataset resources, please refer to https://github.com/usnistgov/PS-002-WALDO.
The challenge dataset relies on two open-source software:
Lightning-Fast Modulation Classification with Hardware-Efficient Neural Networks Link
This challenge uses the RadioML 2018.01A dataset provided by DeepSig. You will be required to provide your contact information to download the dataset.
Please note that we use a fixed train/test split on the dataset for a fair comparison, and you must abide by this in order to provide a valid submission. This split is given by the fixed random seed and other parameters in the Jupyter notebook we provide in the sandbox.
Network anomaly detection based on logs Link
|China Unicom||45||The data is the log data of network equipment, including time stamp, log information, etc. The log is in text format. The data is divided into training set and test set. The training set contains tag information, that is, with exception and without exception; it is a Boolean tag representing a true or false result. The test set comes from the same network equipment as the training set. The test set does not contain tag information. After modelling, the contestants predict the test set and get its tag. The data could not be found.|
Graph Neural Networking Challenge 2021 - Creating a Scalable Network Digital Twin Link
|Barcelona Neural Networking Center||47|
We provide a dataset generated with the OMNet++ network simulator, which is a discrete event packet-level network simulator. The dataset contains samples simulated in several topologies and includes hundreds of routing configurations and traffic matrices. Each sample is labelled with network performance metrics obtained by the simulator: per-flow performance statistics (mean per-packet delay, jitter and loss), and port statistics (e.g. queue utilization, size).
Data is divided in three different sets for training, validation and test. As the challenge is focused on scalability, the validation dataset contains samples of networks considerably larger (51-300 nodes) than those of the training dataset (25-50 nodes). Likewise, the test dataset will be released at the end of the challenge, just before the evaluation phase starts, and it will contain samples following the same distribution as in the validation dataset.
Please, find a detailed description of the datasets and the links to download them here: