Swirls: Sniffing Wi-Fi Using Radios with Low Sampling Rates

Swirls: Sniffing Wi-Fi Using Radios with Low Sampling Rates
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DOI:
10.1145/3565287.3610279
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发表时间:
2023-10
期刊:
Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
影响因子:
--
通讯作者:
Zhihui Gao;Yiran Chen;Tingjun Chen
Zhihui Gao;Yiran Chen;Tingjun Chen
中科院分区:
其他
文献类型:
--
作者:
Zhihui Gao;Yiran Chen;Tingjun Chen

文献摘要

相似文献

下一代Wi-Fi系统采用大信号带宽来实现显着提高的数据速率,同时需要有效的网络监控和频谱共享应用方法。如果无线电接收机能够提取有用的网络信息,如无线分组的持续时间和结构,那么以低采样率工作的无线电接收机(RX)可以大大提高此类系统的能量和成本效率。在本文中,我们提出了swils的设计,这是一个新的框架,用于嗅探Wi-Fi物理层信息,使用rx以比信号带宽小得多的采样率工作。swils包括三个模块定制的低采样率RX:联合包检测,优化的RX频率选择和包属性解码器。我们使用三个软件定义的无线电平台实现了Swirls,并在现实场景中广泛评估了Swirls。实验表明,对于20/40 MHz的802.11n数据包,采样率为5 MHz的Swirls在信噪比仅为10 dB的情况下,传输时间和物理业务数据单位长度解码的平均绝对误差(MAE)分别为0.06 ms和1.91 kB。在相同的设置下,Swirls同时对调制编码方案、空间流数和带宽的分类准确率分别达到95.3%、96.1%和95.6%。在极端情况下,对于160 MHz 802.11ac/ax数据包,采用2.5 MHz采样率(即下采样比为64)的漩涡仍然可以实现0.47/0.67 ms的传输时间解码MAE。
Next-generation Wi-Fi systems embrace large signal bandwidth to achieve significantly improved data rates, while requiring efficient methods for network monitoring and spectrum sharing applications. A radio receiver (RX) operating at low sampling rates can largely improve the energy- and cost-efficiency in such systems if it can extract useful network information such as the duration and structure of wireless packets. In this paper, we present the design of Swirls, a novel framework for sniffing Wi-Fi Physical layer information using RXs operating at sampling rates that are (much) smaller than the signal bandwidth. Swirls consists of three modules tailored for low sampling rate RXs: joint packet detection, optimized RX frequency selection, and packet property decoder. We implement Swirls using three software-defined radio platforms and extensively evaluate Swirls in real-world scenarios. The experiments show that for 20/40 MHz 802.11n packets, Swirls with 5 MHz sampling rate can achieve a mean absolute error (MAE) of transmission time and physical service data unit length decoding of 0.06 ms and 1.91 kB, respectively, at only 10 dB signal-to-noise ratio. With the same setting, Swirls simultaneously achieves a classification accuracy for the modulation and coding scheme, number of spatial streams, and bandwidth of 95.3%, 96.1%, and 95.6%, respectively. In an extreme case for 160 MHz 802.11ac/ax packets, Swirls with 2.5 MHz sampling rate (i.e., a downsampling ratio of 64) can still achieve an MAE of transmission time decoding of 0.47/0.67 ms.