Machine Learning-Assisted Detection for BPSK-modulated Ambient Backscatter Communication Systems

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Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the
direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed
Original languageEnglish
Title of host publicationIEEE Global Communications Conference
Number of pages6
ISBN (Electronic)9781728109626
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventIEEE Global Communications Conference - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019

Publication series

NameIEEE Global Communications Conference
ISSN (Print)2334-0983


ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
Country/TerritoryUnited States


  • Ambient backscatter
  • classification algorithm
  • green communication
  • machine learning
  • signal detection


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