DISC datase

This dataset provides Channel Impulse Response (CIR) measurements from standard-compliant IEEE 802.11ay packets to validate Integrated Sensing and Communication (ISAC) methods. The CIR sequences contain reflections of the transmitted packets on people moving in an indoor environment. They are collected with a 60 GHz software-defined radio experimentation platform based on the IEEE 802.11ay Wi-Fi standard, which is not affected by frequency offsets by operating in full-duplex mode. The dataset is divided into two parts: 1) The first one consists of almost 40 minutes of IEEE 802.11ay CIR sequences including signal reflections on 7 subjects performing 4 different activities, i.e., walking (A0), running (A1), sitting down-standing up (A2), and waving hands (A3). This part is characterized by uniform packet transmission times, with a granularity of over 3 CIR estimates per millisecond, yielding extremely high temporal resolution. 2) In the second part, we use open-source data on Wi-Fi traffic patterns to tune the inter-packet duration and collect more realistic sparse CIR sequences. The resulting CIR measurements, for a total of 9 minutes, are collected with a single subject performing the same 4 activities included in the first part. In the second part, we also use the directional transmission capabilities of our testbed to allow the estimation of the Angle of Arrival (AoA) of the reflections. We envision our dataset being used by researchers to train and validate machine and deep learning algorithms for fine-grained sensing. Possible use cases include, but are not limited to, the extraction of the micro-Doppler signatures of human movement from the CIR, which enables deep learning-based human activity recognition, and person identification from individual gait features. In addition, new ISAC problems such as the sparse reconstruction of sensing parameters from irregularly sampled signal traces, domain adaptation from regularly sampled signals to sparse ones, and target tracking under missing measurements can also be tackled using the provided dataset.本数据集提供符合标准的IEEE 802.11ay数据包的信道冲激响应（Channel Impulse Response, CIR）测量数据，用于验证感知通信一体化（Integrated Sensing and Communication, ISAC）方法。该CIR序列包含了传输数据包在室内环境中移动人体上的反射信号。数据采集基于符合IEEE 802.11ay Wi-Fi标准的60 GHz软件定义无线电实验平台，该平台通过工作在全双工模式下免受频率偏移的影响。 数据集分为两个部分： 1) 第一部分包含近40分钟的IEEE 802.11ay CIR序列，涵盖7名受试者开展4种不同活动时的信号反射，即行走（A0）、跑步（A1）、坐下-站起（A2）以及挥手（A3）。该部分采用均匀的数据包传输时序，每毫秒可生成超过3次CIR估计，具备极高的时间分辨率。 2) 第二部分中，我们利用开源Wi-Fi流量模式数据调整数据包间隔时长，以采集更贴近实际场景的稀疏CIR序列。该部分总时长为9分钟，仅由1名受试者完成第一部分中的4种活动。此外，第二部分还借助实验平台的定向传输能力，实现了反射信号到达角（Angle of Arrival, AoA）的估计。 本数据集可供研究人员用于训练与验证面向细粒度感知的机器学习及深度学习算法。潜在应用场景包括但不限于：从CIR中提取人体运动的微多普勒特征，以此实现基于深度学习的人类活动识别，以及基于个体步态特征进行人员身份识别。此外，利用本数据集还可解决诸多新型ISAC问题，例如从非均匀采样的信号轨迹中稀疏重构感知参数、从规则采样信号到稀疏采样信号的域自适应，以及存在测量缺失时的目标跟踪等。

搜集汇总

背景与挑战

背景概述

DISC数据集是一个基于IEEE 802.11ay标准的毫米波系统集成感知与通信（ISAC）数据集，提供60 GHz室内环境下的信道脉冲响应（CIR）测量数据，用于验证机器学习算法。数据集分为三个部分：DISC-A包含均匀包传输下的多主体活动数据，DISC-B支持多波束成形以实现角度估计和多主体跟踪，DISC-C则模拟真实稀疏流量模式。该数据集适用于人类活动识别、步态识别和稀疏重建等应用，覆盖人工智能、无线网络和信号处理等多个领域。