Rethinking RSSI for WiFi Sensing

TL;DR

This paper demonstrates that RSSI, traditionally viewed as too coarse for sensing, can support practical passive WiFi sensing when interpreted through a power-domain model that links Doppler, AoA, and delay. The authors introduce WiRSSI, a bistatic 1Tx-3Rx framework that extracts Doppler and AoA via a lightweight 2D FFT from RSSI and infers bistatic delay using an amplitude-based relationship calibrated by a single reflection-coefficient ratio $\gamma$. Compared with CSI-based baselines, WiRSSI achieves sub-meter median trajectory errors (roughly 0.78–0.91 m) while CSI methods reach about 0.51–0.60 m, illustrating a meaningful accuracy gap but a compelling low-cost sensing option. The work highlights that RSSI can serve as a viable sensing modality for ISAC in scenarios where CSI is unavailable or impractical, and it suggests hybrid sensing opportunities combining RSSI and CSI measurements for enhanced robustness.

Abstract

The Received Signal Strength Indicator (RSSI) is widely available on commodity WiFi devices but is commonly regarded as too coarse for fine-grained sensing. This paper revisits its sensing potential and presents WiRSSI, a bistatic WiFi sensing framework for passive human tracking using only RSSI measurements. WiRSSI adopts a 1Tx-3Rx configuration and is readily extensible to Multiple-Input Multiple-Output (MIMO) deployments. We first reveal how CSI power implicitly encodes phase-related information and how this relationship carries over to RSSI, showing that RSSI preserves exploitable Doppler, Angle-of-Arrival (AoA), and delay cues associated with human motion. WiRSSI then extracts Doppler-AoA features via a 2D Fast Fourier Transform and infers delay from amplitude-only information in the absence of subcarrier-level phase. The estimated AoA and delay are then mapped to Cartesian coordinates and denoised to recover motion trajectories. Experiments in practical environments show that WiRSSI achieves median XY localization errors of 0.905 m, 0.784 m, and 0.785 m for elliptical, linear, and rectangular trajectories, respectively. In comparison, a representative CSI-based method attains median errors of 0.574 m, 0.599 m, and 0.514 m, corresponding to an average accuracy gap of 0.26 m. These results demonstrate that, despite its lower resolution, RSSI can support practical passive sensing and offers a low-cost alternative to CSI-based WiFi sensing.

Figures (17)

• Figure 1: Comparison of RSSI and CSI during human motion.
• Figure 2: Bistatic geometry of the 1Tx-3Rx WiFi setup.
• Figure 3: Experimental setup of the WiFi sensing system.
• Figure 4: Human motion trajectories: (a) elliptical, (b) linear, and (c) rectangular.
• Figure 5: Elliptical trajectory