Toward Opportunistic Radar Sensing Using Millimeter-Wave Wi-Fi

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, beamwidth, and transmit power, which could limit the sensing accuracy. To investigate such challenges, in this article, we study the feasibility of using the sector-level sweeping (SLS) procedure of IEEE 802.11ad to provide opportunistic indoor radar sensing service, which is vital to smart Internet of Things (IoT) applications. 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 the tradeoffs between sensing accuracy and required sensing resources in terms of system configurations (e.g., antenna array size and the overlapping of neighboring beams) and the impact of signal-to-noise ratio (SNR). Furthermore, based on the human multipath reflections captured from a real-world measurement campaign, we reconstruct the sensing channel, investigate the feasibility of monitoring the gesture behavior in a smart home environment, and discuss some findings and insights.