LiveSense: A Real-Time Wi-Fi Sensing Platform for Range-Doppler on COTS Laptop

Jessica Sanson; Rahul C. Shah; Maximilian Pinaroc; Cagri Tanriover; Valerio Frascolla

eess.SPMar 6, 2026

LiveSense: A Real-Time Wi-Fi Sensing Platform for Range-Doppler on COTS Laptop

Authors: Jessica Sanson, Rahul C. Shah, Maximilian Pinaroc, Cagri Tanriover, Valerio Frascolla

Organizations: ∗ Intel Deutschland GmbH, Munich, Germany · † Intel Labs, Santa Clara, CA, USA

Abstract

We present LiveSense - a cross-platform that transforms a commercial off-the-shelf (COTS) Wi-Fi Network Interface Card (NIC) on a laptop into a centimeter-level Range-Doppler sensor while preserving simultaneous communication capability. The laptops are equipped with COTS Intel AX211 (Wi-Fi 6E) or Intel BE201 (Wi-Fi 7) NICs. LiveSense can (i) Extract fully-synchronized channel state information (CSI) at >= 40 Hz, (ii) Perform time-phase alignment and self-interference cancellation on-device, and (iii) Provide a real-time stream of range, Doppler, subcarrier magnitude/phase and annotated video frames to a Python/Qt Graphical User Interface (GUI). The demo will showcase the ability to detect (i) Distance and radial velocity of attendees within a few meters of the device, (ii) Micro-motion (respiration), and (iii) Hand-gesture ranging. To the best of our knowledge, this is the first-ever demo to obtain accurate range information of targets from commercial Wi-Fi, despite the limited 160 MHz bandwidth.

Figures & tables

Feature	Standard Bistatic Sensing	LiveSense (Proposed)
Topology	2+ Devices (Tx & Rx separated)	Monostatic (Single COTS Laptop)
Sensing Metric	Doppler (Velocity) dominant	Range + Doppler (cm-level)
Interference	Susceptible to env. noise	$>$ 25 dB SNR Gain (via 160MHz)
Calibration	Environment-specific training	Calibration-free (Auto-alignment)
Coexistence	Often drops packets/rates	$>$ 40Hz stable under Video/Call load
Tracking	Single target focus	Multi-target (Range domain separation)

TABLE I : Comparison of LiveSense vs. Standard Bistatic Wi-Fi Sensing

Fig. 1 : LiveSense graphical user interface

Fig. 2 : LiveSense in real-time Range–Doppler heatmap

Fig. 3 : Multi-target estimation in a busy cafe environment. The systemr esolving two distinct targets (0.3 m and 1 m) despite heavy background Wi-Fi traffic, demonstrating robustness against environmental interference.

Wi-Fi Sensing

Wi-Fi sensing uses wireless signal changes to infer human motion, location, presence, and activity without dedicated wearable sensors.

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Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops

Jessica Sanson, Rahul C. Shah, Valerio Frascolla

Intel Deutschland GmbH, Munich, Germany · Intel Corp., Santa Clara, CA, USA

Wi-Fi Sensing

Open paper AskPDF arXiv

Human Presence Detection (HPD) is key to enable intelligent power management and security features in everyday devices. In this paper we propose the first HPD solution that leverages monostatic Wi-Fi sensing and detects user position using only the built-in Wi-Fi hardware of a device, with no need for external devices, access points, or additional sensors. In contrast, existing HPD solutions for laptops require external dedicated sensors which add cost and complexity, or rely on camera-based approaches that introduce significant privacy concerns. We herewith introduce the Range-Filtered Doppler Spectrum (RF-DS), a novel Wi-Fi sensing technique for presence estimation that enables both range-selective and temporally windowed detection of user presence. By applying targeted range-area filtering in the Channel Impulse Response (CIR) domain before Doppler analysis, our method focuses processing on task-relevant spatial zones, significantly reducing computational complexity. In addition, the use of temporal windows in the spectrum domain provides greater estimator stability compared to conventional 2D Range-Doppler detectors. Furthermore, we propose an adaptive multi-rate processing framework that dynamically adjusts Channel State Information (CSI) sampling rates-operating at low frame rates (10Hz) during idle periods and high rates (100Hz) only when motion is detected. To our knowledge, this is the first low-complexity solution for occupancy detection using monostatic Wi-Fi sensing on a built-in Wi-Fi network interface controller (NIC) of a commercial off-the-shelf laptop that requires no external network infrastructure or specialized sensors. Our solution can scale across different environments and devices without calibration or retraining.

Abstract

Wi-Fi Sensing

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