Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

by   Chang Liu, et al.

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.


page 5

page 6

page 10

page 11

page 16

page 18

page 24

page 28


Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications

Existing tag signal detection algorithms inevitably suffer from a high b...

Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications

Channel estimation is a challenging problem for realizing efficient ambi...

A Signal Detection Scheme Based on Deep Learning in OFDM Systems

Channel estimation and signal detection are essential steps to ensure th...

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

This article introduces a novel lightweight framework using ambient back...

Complex CNN CSI Enhancer for Integrated Sensing and Communications

In this paper, we propose a novel complex convolutional neural network (...

Symbol Detection of Ambient Backscatter Systems with Manchester Coding

Ambient backscatter communication is a newly emerged paradigm, which uti...

Deep Learning-empowered Predictive Precoder Design for OTFS Transmission in URLLC

To guarantee excellent reliability performance in ultra-reliable low-lat...

Please sign up or login with your details

Forgot password? Click here to reset