Channel Estimation for Full-Duplex Multi-Antenna Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

Automated summary

Ambient backscatter communication (AmBC) is a promising technology for low-cost, low-power devices in the Internet-of-Things (IoT) applications. This paper addresses the challenging channel estimation problem for full-duplex multi-antenna AmBC systems. Three solutions are proposed, including a pilot-based maximum likelihood (ML) estimator, a semi-blind estimator based on the expectation maximization (EM) framework, and a semi-blind estimator based on the decision-directed (DD) strategy. Simulations demonstrate the accuracy and performance superiority of the semi-blind estimators compared to the ML estimator.