Channel Estimation for Full-duplex Multi-tag Ambient Backscatter Communication Systems with I/Q Imbalance

TL;DR

This paper introduces a novel channel estimation scheme for full-duplex multi-tag ambient backscatter communication systems, addressing hardware impairments like I/Q imbalance to improve accuracy and system performance.

Contribution

It proposes a three-stage training protocol and two semi-blind estimators, enhancing estimation accuracy while managing complexity in multi-tag AmBC systems with hardware impairments.

Findings

The proposed estimators approach their respective Cramer-Rao bounds.

The three-stage training protocol ensures signal orthogonality and avoids error floors.

Semi-blind estimators outperform pilot-based estimation in accuracy.

Abstract

Ambient backscatter communication (AmBC) has emerged as a highly attractive paradigm for energy-efficient communication. Full-duplex multi-tag AmBC systems provide the scalability and efficient spectrum utilization essential for next generation Internet-of-Things (IoT) networks. However, the presence of multiple tags, self-interference and hardware impairments such as inphase/quadrature (I/Q) imbalance, makes accurate channel estimation indispensable for efficient interference management. The large number of channel parameters and the presence of mirror images of each signal component necessitate careful design of the channel estimation phase to prevent performance degradation. In this work, we propose a novel three-stage training protocol and pilot-based estimation scheme that ensure signal orthogonality and successfully avoid error floors. We also propose two semi-blind estimators, one based on decision-directed (DD) criterion and the other on the expectation conditional maximization (ECM) framework. By exploiting both pilots and data symbols, these two estimators achieve higher estimation accuracy than pilot-based estimation, at the cost of additional complexity. Cramer-Rao bounds (CRBs) for both types of estimation are also derived. The pilot-based estimator and the ECM estimator approach their respective CRBs, while the DD estimator performs mid-way between them. The three proposed solutions support different use cases by offering distinct tradeoffs between performance and computational complexity.