DISC: a dataset for integrated sensing and communication in mmWave systems

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.