Machine Learning-Based Blind Signal Detection for Ambient Backscatter Communication Systems

Abstract

Ambient backscatter communication (AmBC) is emerging as a promising energy-saving and spectrum-efficient passive Internet of Things (IoT) technology that can be used for battery-less communication devices due to its low power consumption and cost constraints. However, in AmBC systems, recovering tag information at the reader is a challenging problem due to the difficulty in obtaining relevant channel state information (CSI). In this paper, we propose a variational Bayesian inference and machine learning (VBI-ML) based blind signal detection method, which can automatically recover tag information in AmBC systems. Firstly, two known labels are transmitted from the tag to the reader before valid data is transmitted, thus eliminating the need for CSI estimation. Secondly, we use the VBI approach with the Gaussian mixture model to obtain the real constellation information, which does not require a priori signal modulation technology to recover the tag information at the reader automatically. Using real constellation information, the signal detection problem is converted into a clustering problem. Finally, we cluster all the received signals using an improved expectation maximization algorithm in ML to learn the parameters in labeled and unlabeled signals and recover the signals. Thorough simulation results demonstrate that our proposed method performs similarly to the optimal detector with perfect CSI and outperforms traditional constellation learning methods. More critically, ML algorithms can mitigate direct link interference and simplify the number of estimated parameters.