Enhanced Channel Estimation for Multi-tag Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a highly promising communication paradigm for the next generation, energy efficient Internet-of-Things (IoT) applications. In this work, we consider the problem of channel estimation for multi-tag AmBC systems. Recent works have proposed pilot-based solutions for this problem, which incur a significant training overhead. In this work, we propose a novel semi-blind algorithm for channel estimation in multi-tag AmBCs. The proposed algorithm is based on the expectation maximization framework and incorporates both pilot sequences and data symbols into the estimation process. Our simulations demonstrate the mean-squared error performance of the proposed scheme, showing that it yields substantially higher estimation accuracy than the state of the art.