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Abstract
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.
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.
Original language | English |
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Title of host publication | IEEE Global Communications Conference |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 9781728109626 |
DOIs | |
Publication status | Published - 2019 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Global Communications Conference - Waikoloa, United States Duration: 9 Dec 2019 → 13 Dec 2019 |
Publication series
Name | IEEE Global Communications Conference |
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ISSN (Print) | 2334-0983 |
Conference
Conference | IEEE Global Communications Conference |
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Abbreviated title | GLOBECOM |
Country/Territory | United States |
City | Waikoloa |
Period | 09/12/2019 → 13/12/2019 |
Keywords
- Ambient backscatter
- classification algorithm
- green communication
- machine learning
- signal detection
Fingerprint
Dive into the research topics of 'Machine Learning-Assisted Detection for BPSK-modulated Ambient Backscatter Communication Systems'. Together they form a unique fingerprint.Projects
- 2 Finished
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Adaptive ambient backscatter Communications for ultra-low power Systems
Jäntti, R., Badihi Olyaei, B., Ruttik, K., Menta, E., Sheikh, M. & Wang, X.
01/09/2018 → 31/08/2021
Project: Academy of Finland: Other research funding
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AIMHIS: Collaborative Research: Ambient Re-Scatter inspired Machine Type Communications for Heterogeneous IoT Systems
Jäntti, R. & Duan, R.
12/04/2017 → 31/12/2019
Project: Academy of Finland: Other research funding