Machine Learning Assisted Signal Detection in Ambient Backscatter Communication Networks

2021 ◽  
Author(s):  
Usman Saleh Toro ◽  
Basem M. Elhalawany ◽  
Aslan B Wong ◽  
Lu Wang ◽  
Kaishun Wu
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Communication Networks ◽  
Backscatter Communication ◽  
Ambient Backscatter

The paper describes ML techniques that can be leveraged to assist signal detection in ambient backscatter communication networks.

scholarly journals Machine Learning Assisted Signal Detection in Ambient Backscatter Communication Networks

2021 ◽  
Author(s):  
Usman Saleh Toro ◽  
Basem M. Elhalawany ◽  
Aslan B Wong ◽  
Lu Wang ◽  
Kaishun Wu
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Communication Networks ◽  
Backscatter Communication ◽  
Ambient Backscatter

The paper describes ML techniques that can be leveraged to assist signal detection in ambient backscatter communication networks.

Ambient Backscatter Communication Networks

2020 ◽  
Author(s):  
Dinh Thai Hoang ◽  
Dusit Niyato ◽  
Dong In Kim ◽  
Nguyen Van Huynh ◽  
Shimin Gong
Keyword(s):  
Communication Networks ◽  
Backscatter Communication ◽  
Ambient Backscatter

Multiantenna Receiver Signal Detection in AmBC Based on Cluster Analysis

2020 ◽  
Vol 63 (6) ◽  
pp. 844-856
Author(s):  
Jumin Zhao ◽  
Liang Zhang ◽  
Deng'ao Li
Keyword(s):  
Signal Detection ◽  
Communication Channel ◽  
Theoretical Research ◽  
Channel State ◽  
Detection Mechanism ◽  
Channel Detection ◽  
Backscatter Communication ◽  
Clustering Center ◽  
Short Time ◽  
Ambient Backscatter

Abstract Ambient backscatter communication (AmBC) has great application prospects in the green Internet of Things due to its shared nature of energy and spectrum. In this paper, we have done research on the signal detection problem in the AmBC system and proposed to use the k-means clustering analysis algorithm to detect the received backscattered signal. At the same time, in order to obtain a more obvious clustering center, this paper introduces a multi-antenna receiver channel detection mechanism. The proposed method can compare and analyze the energy received by the receiver antennas in an extremely short time slots. The channel state information between the reader and the backscatter tag is obtained, and the optimal communication channel is selected, thereby effectively improving the clustering effect. Finally, a large number of experimental simulations are provided to compare and analyze the corresponding BER performance to confirm our theoretical research.

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