26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction

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.<br>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.本数据集为配套于论文《面向域不变阻塞预测的26 GHz正交频分复用与77 GHz调频连续波雷达数据集》的雷达与真值数据集。该论文的摘要如下。本文中另一部分通信正交频分复用（Orthogonal Frequency Division Multiplexing, OFDM）数据集的相关信息可在已公开的学术论文中获取。 本论文提出了一种新型毫米波通信（millimeter wave communication）与雷达感知共存数据集。本次实测工作针对覆盖多样化人体活动的阻塞预测场景展开。实验配置了26 GHz正交频分复用多波束通信测试平台，以及77 GHz调频连续波（Frequency-Modulated Continuous-Wave, FMCW）多输入多输出（Multiple Input Multiple Output, MIMO）雷达多静态配置架构。 研究团队对对应的双静态（bistatic）信道状态信息（Channel State Information, CSI）与多静态反向散射信道（multi-monostatic backscattered channel）完成了预处理，并通过可视化预处理样本检查的方式开展初步域偏移（Domain Shift）分析。当模型训练数据的采集测量环境与模型推理阶段的测量环境存在显著差异时，阻塞预测模型便会出现域偏移问题。域偏移会导致模型在推理阶段的性能下降。此前的毫米波阻塞预测相关研究均未考虑在预测模型中缓解域偏移问题，我们认为这一现状的成因在于，目前尚无在大量不同测量环境下采集样本的毫米波阻塞预测专用数据集。 分析结果显示，在不同测量环境下采集的预处理雷达反向散射信道样本中，特征信号的存在水平存在差异；同时，经预处理的OFDM测试平台信道状态信息样本中，多普勒bin（doppler bin）的能量幅值与位置也呈现出不同分布。因此，构建一个规模足够大的阻塞预测数据集，使其样本采集于能够在模型训练与推理场景间产生足够显著域偏移的多样化测量环境下，对于开展模型域偏移缓解相关研究具有重要意义。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于域偏移不变阻塞预测的毫米波雷达数据集，包含26 GHz OFDM通信测试台和77 GHz FMCW MIMO雷达的多单静态设置数据，以及地面实况视频。数据集旨在支持机器学习模型在变化测量环境下缓解域偏移的研究，涉及不同人类活动，适用于通信与传感集成（ISAC）领域。