Leggiero: Analog WiFi Backscatter with Payload Transparency

TL;DR

This work addresses the challenge of battery-free sensing in AIoT by removing traditional μP-based sensor interfacing and enabling seamless coexistence with ambient WiFi traffic. It introduces Leggiero, an analog WiFi backscatter system that directly converts sensor voltages to RF phase via a passive varactor-based reflective circuit, embedding this phase into the CSI through ESS in 802.11n. The receiver decodes the embedded sensor data by comparing ESS CSI with regular CSI, achieving payload transparency and maintaining WiFi throughput, with an ASIC tag power of about $30\,\mu$W at 400 Hz sampling and a measured throughput of roughly $5\,\text{Kbps}$. The results demonstrate substantial power savings ($\sim$4–5×) over prior digital backscatter schemes and confirm robust operation in LOS and NLOS conditions, opening a path toward battery-free, plug-and-play analog sensing in commodity WiFi networks.

Abstract

Backscatter is an enabling technology for battery-free sensing in today's Artificial Intelligence of Things (AIOT). Building a backscatter-based sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier's traffic. In order to address the above issues, in this paper, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency. Leveraging a specially designed circuit with a varactor diode, this design avoids using a microprocessor to interface between the radio and the sensor, and directly converts the analog sensor signal into the phase of RF (radio frequency) signal. By carefully designing the reference circuit on the tag and precisely locating the extra long training field (LTF) section of a WiFi packet, Leggiero embeds the analog phase value into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement Leggiero design and evaluate its performance under varied settings. The results show that the power consumption of the Leggiero tag (excluding the power of the peripheral sensor module) is 30uW at a sampling rate of 400Hz, which is 4.8* and 4* lower than the state-of-the-art WiFi backscatter schemes. The uplink throughput of Leggiero is suficient to support a variety of sensing applications, while keeping the WiFi carrier's throughput performance unaffected.

Figures (26)

• Figure 1: Leggiero eliminates the need for using microprocessors ($\bm{\mu}$P) and works transparently with the WiFi carrier's traffic.
• Figure 2: Extra spatial sounding featured 802.11n packet. It provides a duplicated CSI since the long-training-fields (LTF) experience the same channel.
• Figure 3: The reflection coefficient $\Gamma$. (a) shows its definition, where $Z_0$ and $Z_L$ are the impedance of the transmission line and the load, respectively. (b) shows its polar coordinates representation in a Smith chart.
• Figure 4: The shorted variable capacitor model and the corresponding phase variation. Higher capacitance leads to a smaller phase range and worse linearity.
• Figure 5: Simulated phase and magnitude of the reflected signal v.s. input voltage. The phase is near-linear in 0-5V with little attenuation.