CSI Dataset towards 5G NR High-Precision Positioning

Citation Author(s):
Kaixuan
Gao
Harbin Engineering University: Harbin, Heilongjiang, CN
Huiqiang
Wang
Harbin Engineering University: Harbin, Heilongjiang, CN
Hongwu
Lv
Harbin Engineering University: Harbin, Heilongjiang, CN
Submitted by:
Kaixuan Gao
Last updated:
Tue, 08/03/2021 - 02:56
DOI:
10.21227/jsat-pb50
Data Format:
License:
0
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Abstract 

This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R16 standards-complied.

 

5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).

Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G  NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.

However, 5G Location studies are facing great obstacles due to the lack of commercialized 5G ISAC-base-stations that support positioning functions, as well as publicly available datasets.

 

To overcome this datasets deficiency, we make our 5G NR POSITIONING DATASET publicly available.

This dataset can be used for indoor positioning, indoor-outdoor-integrated positioning, NLoS, 5G channel estimation and other types of research, providing researchers with CSI-level position-related feature data.

 

If you'd like to learn more about our dataset, spend some time going through our paper for the model overview, generation method, 5G NR reference signal and mant other subjects.

Also, we set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Contact Us gkx@hrbeu.edu.cn

 

keywords: integrated sensing and communicatio, ISAC, 5G, New Radio, 5G NR, massive MIMO, indoor Localization, indoor positioning, 5G positioning, 5G localization, CSI, channel statement information, ray-tracing, ray tracing, Machine Learning, Deep Learning, CNN, DNN, mmWave, sub 6GHZ, 3GPP

Instructions: 

 

The dataset_[SNR]_[date]_[time].mat contains: 

1) a 4-D matrix, features, representing the feature data, and

2) a structure array, labels, labeling the ground truth of UE positions.

[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.

The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).

The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.

The value of Ng corresponds to the number of UEs in labels.

 

 Colsed beta test is running.

In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.

 

In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.

 

Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.

To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.

 

 Release note 

2021-07-23 :

1) Recruit participants for colsed beta test.

2021-07-22 :

1)Expend our dataset with more CSI data with low SNR levels noise.

2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Closed beta test will start after suggestion collection.

2021-07-18 :

1)Expend our dataset with more CSI data with different SNR levels noise.

2)Publish map files for Scenario 1 indoor office.

 

 

Comments

where is the paper?

Submitted by Pedro Macedo on Tue, 08/10/2021 - 10:52