A Single-Chain Backscatter Tag for Multi-Sensor Multiplexing

TL;DR

MATRIX is a novel single-chain backscatter tag that multiplexes multiple sensors using voltage-division and PWM encoding, enabling efficient, low-power multi-sensor measurements at a single site with robust demultiplexing.

Contribution

It introduces a voltage-division multiplexing architecture and a Hidden Markov Model-based demultiplexing method for multi-sensor backscatter tags, supporting multiple sensors with a single analog chain.

Findings

Supports five sensors with 20 dB SNR at 30 kHz sampling

Consumes 25.56 microWatts in ASIC implementation

Validated in plant, health, and microphone sensing scenarios

Abstract

Many real-world sensing tasks require co-located, multi-modal measurements at a single site, typically a bundle of two to five sensors, for example, in plant stress sensing and blood pressure estimation. RF-backscatter devices have emerged as a low-power solution for sensing, yet existing backscatter tags support a single sensor. Placing several single-sensor tags at one site increases attachment footprint and induces mutual coupling between nearby tag antennas, thereby limiting practical deployment. We present MATRIX, a single-chain multi-sensor backscatter tag that concurrently supports multiple onboard sensors and multiplexes them as a composite voltage, then backscatters it through one analog modulation chain. Rather than time-division polling, which introduces inter-sensor sampling offsets, or frequency-division, which requires independent per-sensor modulation chains, MATRIX introduces a voltage-division multiplexing architecture in which each sensor value is encoded as a PWM waveform, carrying the measurement in its duty cycle and reserving the amplitude for multiplexing. To support reliable demultiplexing, MATRIX selects the voltage-division weights in a binary-weighted geometric progression so that every active-sensor set maps to a uniquely invertible, well-spaced composite voltage. The composite voltage is then converted into backscatter frequency shifts through a single modulation chain. At the receiver, MATRIX formulates demultiplexing as a Hidden Markov Model to recover per-sensor readings while tolerating analog hardware imperfections and multipath. MATRIX's ASIC design consumes 25.56uW. Detailed evaluation shows that the prototype, multiplexing five sensors, achieves 20 dB average signal reconstruction SNR at a 30 kHz sampling frequency; we further validate MATRIX with case studies in plant sensing, health monitoring, and microphone-based direction finding.