Channel Estimation for Backscatter Communication Systems Under Circuit Sensitivity Constraint

Backscatter communication (BC) has emerged as a promising paradigm for enabling energy-efficient and low-cost Internet of Things (IoT) systems. However, one practical constraint that is often neglected in BC studies is the circuit sensitivity of the backscatter device (BD), which sets the minimum signal strength required to activate it for signal reflection. In this paper, we address the channel estimation (CE) problem in the BC system with a circuit sensitivity constraint. Specifically, we propose three channel estimators for the receiver in the BC system that is unaware of the activation states of the BD, namely the maximum likelihood (ML), linear minimum mean square error (LMMSE), and maximum a posteriori (MAP) estimators. We derive both the Cram
$\acute{\text {e}}$
r-Rao bound (CRB) and Bayesian Cram
$\acute{\text {e}}$
r-Rao bound (BCRB) to evaluate the performance of the proposed estimators. Finally, we provide simulations to validate our analysis. Our results demonstrate that the proposed LMMSE and MAP estimators outperform traditional estimators that neglect the circuit sensitivity constraint.