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Towards Opportunistic Radar Sensing Using Millimeter-wave WiFi



Jian Wang, Jack Chuang, Samuel Berweger, Camillo Gentile, Nada T. Golmie


Sensing with communication waveforms has drawn growing interest thanks to the ubiquitous availability of wireless networks. However, the required sensing resources may not always be available in a communication system. In addition, the communication system may have limited bandwidth, antenna size, and transmit power, which could limit the sensing accuracy. To investigate such challenges, in this paper we study the feasibility of using the sector-level sweeping (SLS) procedure of IEEE 802.11ad to provide the opportunistic indoor radar sensing. In particular, we design a framework to estimate the target's spatial position with respect to delay and angle, by employing the Multiple Signal Classification (MUSIC) super-resolution algorithms. We conduct an extensive performance evaluation to understand performance trade-offs in terms of system configurations (e.g., antenna array size, the overlapping of neighboring beams) and the impact of signal-to-noise ratio (SNR). Furthermore, based on the human multi-path reflections captured from a real-world measurement campaign, we reconstruct the sensing channel, demonstrate the feasibility of monitoring the gesture behavior, and discuss some findings and insights.
IEEE Internet of Things Journal


Channel estimation, gesture recognition, IEEE 802.11ad, mmWave, WiFi sensing, MUSIC, opportunistic radar sensing.


Wang, J. , Chuang, J. , Berweger, S. , Gentile, C. and Golmie, N. (2024), Towards Opportunistic Radar Sensing Using Millimeter-wave WiFi, IEEE Internet of Things Journal, [online], (Accessed March 5, 2024)
Created January 1, 2024, Updated January 16, 2024