IEEE 802.11bf DMG Sensing: Enabling High-Resolution mmWave Wi-Fi Sensing

Abstract

IEEE 802.11bf amendment is defining the wireless Local Area Network (WLAN) sensing procedure, which supports sensing in license-exempt frequency bands below 7 GHz, and the Directional Multi-Gigabit (DMG) sensing procedure for license-exempt frequency bands above 45 GHz. In this paper, we examine the use of Millimeter-Wave (mmWave) Wi-Fi to enable high-resolution sensing. We first provide an introduction to the principle of sensing and the modifications defined by the IEEE 802.11bf amendment to IEEE 802.11 to enable mmWave Wi-Fi sensing. We then present a new open-source framework that we develop to enable the evaluation of the DMG sensing procedure accuracy. We finally quantify the performance of the DMG sensing in terms of the velocity/angle estimate accuracy, and its overhead on the communication link. Results show that the DMG sensing procedure defined in IEEE 802.11bf is flexible enough to accommodate a wide range of sensing applications. For the bistatic scenario considered, the velocity accuracy is in the interval 0.1 m/s to 0.4 m/s, while the angular accuracy is between 1 degree and 8 degrees depending on the sensing parameters used. Ultimately, the overhead introduced by sensing is limited with a sensing overhead below 5.5 % of the system symbol rate.

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“…The standardization Task Group for IEEE 802.11bf has introduced enhancements dedicated to channel estimates, including training sequences and the beam refinement protocol (BRP) for the sensing procedure. The general principle of radar sensing and the trade-off between sensing accuracy and overhead are explained in [149]. Radar technique aims to achieve resolution in separating objects or people in terms of range, angle, or velocity.…”

Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

“…But in addition to simply delineating the body, the keypoints of the Boulic stickman are temporally correlated so that they move in unison to mimic the human gait, provided the trajectory of the body (defined by the position of a single reference keypoint over time), the limb speed, and the dimensions of the body (height, width, and depth). The Boulic stickman is most suitable for generating realizations of the walking use case for human sensing that we consider in other work [11], [38]. For the gesture recognition use case that we consider here, the original stickman was modified to allow the hands to move independently -each hand within the sphere circumscribed by the maximum extension of the arm -while maintaining correlation between the other keypoints.…”

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