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.本数据集为论文《面向域不变阻塞预测的26 GHz正交频分复用（OFDM）与77 GHz调频连续波（FMCW）雷达数据集》所关联的雷达与基准真值（groundtruth）数据集。该论文的摘要如下。本文中通信OFDM数据集其余部分的相关信息，可在公开可获取的对应论文中查阅。 本论文提出了一种新型毫米波通信（comms）与雷达感知共存数据集。本次测量实验针对多样化人类活动下的阻塞预测场景开展。实验配置了26 GHz正交频分复用（OFDM）多波束通信测试平台，以及77 GHz调频连续波（FMCW）多输入多输出（MIMO）雷达多静态部署架构。针对对应双静态信道状态信息与多静态反向散射信道，我们通过可视化预处理样本抽检的方式完成了预处理，以开展初步的域偏移分析。 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。迄今为止，尚无毫米波阻塞预测相关研究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，尚未存在可覆盖大量不同测量环境下采集样本的毫米波阻塞预测数据集。 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；同时预处理后的OFDM测试平台信道状态信息样本中，多普勒单元（doppler bin）的能量幅值与位置也各不相同。 因此，构建一个规模足够大的阻塞预测数据集，使其样本采集自能够在模型训练与推理场景间引发显著域偏移的不同测量环境，对于开展模型域偏移缓解研究具有重要意义。