Radio Access

ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN

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Federated Learning for Spatial Reuse in a multi-BSS (Basic Service Set) scenario Link

Radio Link Failure Prediction Link

Build-a-thon(PoC) Network resource allocation for emergency management based on closed-loop analysis Link

Powerpoint slides for the progress of teams during the challenge

Link

ITU-ML5G-PS-011: Combinatorial Optimization Challenge:- Delivery route optimization Link

The dataset is provided in the Dataset module.
Each sample in the dataset is composed of 2 elements:

1. A json object that describes the graph of the transportation network, see the below example:
   {
   nodes:[0,1,2] ,# id list of the transportation nodes
   edges:[
   (0,1):{ # (starting point, end point)
   lanes: [0,1,2], # id list of the lanes, the length of the array is the number of the lanes between the two adjacent nodes
   lane_weights:[10, 10, 20] # The weight that each lane can carry
   }
   ]
   }
2. A json object that describes the starting point, endpoint, weight information of each delivery, like:[{“a_end”: 138, “z_end”: 206, “weight”: 1}, {“a_end”: 74, “z_end”: 156, “weight”: 1}]

Forecasting Model for Service Allocation Network Using Traffic Recognition Link

RF-Sensor Based Human Activity Recognition Link

Github with Data Download:
https://github.com/ci4r/CI4R-Activity-Recognition-datasets

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

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.
Example: “gen_channel.exe train 0 1 2” generates the first 3 samples of the “train” dataset.

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

WALDO (Wireless Artificial intelligence Location DetectiOn): sensing using mmWave communications and ML. Link

For software and dataset resources, please refer to https://github.com/usnistgov/PS-002-WALDO.

The challenge dataset relies on two open-source software:

1. IEEE 802.11ay packet generator available at: https://github.com/usnistgov/PS-002-WALDO.
2. NIST Q-D channel realization software: https://github.com/wigig-tools/qd-realization used to generate the millimeter wireless channel.

Training Dataset
The training datasets consist of a collection of received packets at different SNR. The IEEE 802.11ay preamble is filtered through the NIST Q-D channel realizations, which model the millimeter wave wireless signal propagation in a room with multiple people moving.

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 this dataset is released by DeepSig under the Creative Commons Attribution - NonCommercial - ShareAlike 4.0 License and you must abide by the terms of this license to participate in our challenge.\

Train/test split

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

Graph Neural Networking Challenge 2021 - Creating a Scalable Network Digital Twin Link