Parallel Backscatter: Channel Estimation and Beyond

Abstract

As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal's moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves 2 ~ 4× improvement in the tracking accuracy, compared with the state-of-the-art approach.