Ambient backscatter communication (AmBC) is a thriving paradigm of wireless communication. It enables wireless sensors such as tags to harvest energy from the ubiquitous radio frequency (RF) energy and to communicate without batteries, thus enlarging the service life and cutting down the cost of wireless sensors. It stimulates the revolution of the IoT and has received much attention from academia and industry. However, the sensitivity, a practical constraint below which the backscatter device can not be activated to transmit data, is often overlooked in basic and applied research. In this paper, we study the channel estimation problem of the AmBC system with sensitivity constraint. We first formulate the system model of the AmBC system with sensitivity constraint, then give the structure of the two-part training sequence. After that, we introduce the maximum-likelihood (ML) channel estimator and propose an optimal training design to minimize the channel estimation mean-square error. Finally, simulation results are provided to validate our analysis.


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    Title :

    Channel Estimation and Optimal Training Design for Ambient Backscatter Communication Systems under Sensitivity Constraint


    Contributors:
    Cui, Ziqi (author) / Wang, Gongpu (author) / Wei, Xusheng (author) / Xu, Rongtao (author) / Chen, Xia (author)


    Publication date :

    2022-09-01


    Size :

    688217 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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