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Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
[article]
2020
arXiv
pre-print
Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we
arXiv:2009.05231v1
fatcat:honk3y4gobdyjpssq4el5lsexa