毫米波感知空时减少，手势识别准确率几乎不受影响。
Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?
- 通过子采样模拟减少感知时间，保持高精度手势识别
- 仅25%感知时长使准确率下降0.15个百分点
- 适合无线扩展现实等对低延迟高吞吐有要求的应用
大多数集成感知与通信（ISAC）系统需在两种模式间分配空时。然而，这一决策对感知性能的具体影响尚不明确且研究不足。本文针对使用毫米波（mmWave）ISAC系统的手势识别系统展开研究。基于两台mmWave设备在受试者执行不同手势时进行恒定波束扫描所获取的每波束对功率数据，我们采用卷积神经网络训练手势分类器。随后对测量数据进行子采样，模拟减少感知空时，结果表明，仅使用25%的感知时间，分类准确率相比全时感知仅降低0.15个百分点。此外，毫米波系统具备极高的数据吞吐量，使得毫米波ISAC成为真正无线扩展现实等应用的理想技术支撑。
原文摘要 · Abstract (English)
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.
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