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 develop a DTL
detection framework which consists of offline learning, transfer learning, and
online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is
derived based on the minimum error probability (MEP) criterion. As a
realization of the developed framework, we then apply convolutional neural
networks (CNN) to intelligently explore the features of the sample covariance
matrix, which facilitates the design of a CNN-based algorithm for tag signal
detection. Exploiting the powerful capability of CNN in extracting features of
data in the matrix formation, the proposed method is able to further improve
the system performance. In addition, an asymptotic explicit expression is also
derived to characterize the properties of the proposed CNN-based method when
the number of samples is sufficiently large. Finally, extensive simulation
results demonstrate that the BER performance of the proposed method is
comparable to that of the optimal detection method with perfect CSI.

本文提出了一种基于深度迁移学习 (CNN) 和最小误差概率标准 (MEP) 的 DTL 检测框架，旨在消除通道估计需求以提高 AmBC 系统的性能，结果表明该方法的误码率性能可与完美 CSI 的最优检测方法相媲美。