The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/22117145

Miao, Yang; Berlo, van, Bram; Mao, Kai; Hersyandika, Rizqi; Pollin, Sofie et. al. (2023): 26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/22117145.v2

Dataset

x choose version: version 1 - 2023-03-01

• Electrical and Electronic Engineering
• Communications Technologies
• Engineering
• Technology

Domain Shift, Human Blockage Prediction, Integrated sensing and communication (ISAC), Joint Communication and Sensing, Machine Learning (ML)

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Yang Miao , Bram Berlo, van, Kai Mao, Rizqi Hersyandika, Sofie Pollin, Nirvana Meratnia, Ben Willetts

This dataset is the radar and groundtruth dataset linked to the paper "26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction". The abstract of this paper is blow. The infor of the other part of the communication OFDM dataset in this paper can be found in the paper that is openly accessible.

This paper presents a novel millimeter wave communication (comms) and radar sensing co-existing dataset. The measurement campaign was performed for blockage prediction with diverse human activities. 26 GHz Orthogonal Frequency Division Multiplexing (OFDM) multi-beam communication testbed and 77 GHz Frequency-Modulated Continuous-Wave (FMCW) multiple input, multiple output (MIMO) radar multi-monostatic set-up were configured. The corresponding bistatic channel state information and multi-monostatic backscattered channels are pre-processed for preliminary domain shift analysis by means of visual pre-processed sample inspection. Domain shift inside a blockage prediction model occurs when measurement circumstances under which model training data was collected significantly differ from the model inference measurement circumstances. Domain shifts cause model performance deterioration in the inference phase. No previous millimeter wave blockage prediction research considers mitigating domain shift in prediction models. We argue that this is caused by no millimeter wave blockage prediction datasets being available with samples collected under a large number of different measurement circumstances. Analysis results indicate presence of different signature presence levels in pre-processed radar backscattered channel samples and different doppler bin energy magnitudes and locations in pre-processed OFDM testbed channel state information samples captured under varying measurement circumstances. Therefore, creating a large enough blockage prediction dataset with samples captured under varying measurement circumstances that induce hard enough domain shifts between model train and inference situations is important to allow model domain shift mitigation research. 

• 2023-03-01 first online

• 2023-07-07 published, posted

4TU.ResearchData

*.bin; *.mov

• https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias4005390&context=SearchWebhook&vid=32KUL_KUL%3ALirias&search_scope=lirias_profile&tab=LIRIAS&adaptor=SearchWebhook&lang=en

• MSCA-IF-2020 - Individual Fellowships V.I.P. (Grant agreement ID: 101026885) and Dutch SectorPlan

University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Radio Systems research group;

TU Eindhoven, Department of Mathematics and Computer Science;

Katholieke Universiteit Leuven, Networked Systems