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.
Abstract (translated)
大多数集成传感和通信（ISAC）系统需要在其两种模式之间分配空中时间。然而，这一决策对感知性能的具体影响尚不清楚且未被充分研究。因此，在本文中，我们调查了手势识别系统在使用毫米波（mmWave）ISAC系统时所受到的影响。通过收集两台进行连续波束扫描的mmWave设备的数据集，并记录测试对象执行不同手势期间每个波束对的能量值，我们利用卷积神经网络训练了一个手势分类器。然后，通过对这些测量数据进行子采样，模拟了减少感知空中时间的情况，结果显示，当感知空中时间为25%时，仅使全时段感知下的分类准确率下降0.15个百分点。此外，在这种低空中时间内仍能实现高质量的感知，mmWave系统还因其极高的数据吞吐量而著称，这使得毫米波ISAC成为支持如真正无线扩展现实等应用的理想选择。
URL
https://arxiv.org/abs/2601.10733
