Constellation Learning-Based Signal Detection for Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2019-02, Vol.37 (2), p.452-463
Main Authors: Zhang, Qianqian, Guo, Huayan, Liang, Ying-Chang, Yuan, Xiaojun
Format: Article
Language:English
Subjects:
Algorithms
Ambient backscatter communication (AmBC)
Backscatter
Backscattering
Clustering
Clustering algorithms
Computer simulation
constellation learning
Constellations
Data transmission
Detectors
expectation maximization algorithm
Feature extraction
Machine learning
Modulation
Optimization
Parameters
Radio frequency
Signal detection
Transceivers
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Summary:Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2018.2872382