Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?

TL;DR

This study evaluates how reducing sensing airtime in mmWave ISAC systems affects gesture recognition accuracy, finding minimal impact even at 25% airtime, thus supporting efficient high-throughput sensing for applications like wireless XR.

Contribution

It demonstrates that low sensing airtime in mmWave ISAC systems maintains high gesture recognition accuracy, highlighting the potential for efficient, high-throughput sensing in practical applications.

Findings

25% sensing airtime reduces accuracy by only 0.15 percentage points

High data throughput is achievable alongside effective gesture recognition

mmWave ISAC enables wireless XR applications with minimal sensing overhead

Abstract

Most Integrated Sensing and Communications (ISAC) systems require dividing airtime across their two modes. However, the specific impact of this decision on sensing performance remains unclear and underexplored. In this paper, we therefore investigate the impact on a gesture recognition system using a Millimeter-Wave (mmWave) ISAC system. With our dataset of power per beam pair gathered with two mmWave devices performing constant beam sweeps while test subjects performed distinct gestures, we train a gesture classifier using Convolutional Neural Networks. We then subsample these measurements, emulating reduced sensing airtime, showing that a sensing airtime of 25 % only reduces classification accuracy by 0.15 percentage points from full-time sensing. Alongside this high-quality sensing at low airtime, mmWave systems are known to provide extremely high data throughputs, making mmWave ISAC a prime enabler for applications such as truly wireless Extended Reality.