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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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Organisaatiot

Kuvaus

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
approach.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoIEEE Global Communications Conference
TilaHyväksytty/In press - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Global Communications Conference - Waikoloa, Yhdysvallat
Kesto: 9 joulukuuta 201913 joulukuuta 2019

Julkaisusarja

NimiIEEE Global Communications Conference
ISSN (painettu)2334-0983

Conference

ConferenceIEEE Global Communications Conference
LyhennettäGLOBECOM
MaaYhdysvallat
KaupunkiWaikoloa
Ajanjakso09/12/201913/12/2019

ID: 40522262