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Signal detection with co-channel interference using deep learning

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Liu, Chenguang, Chen, Yunfei and Yang, Shuang-Hua (2021) Signal detection with co-channel interference using deep learning. Physical Communication, 47 . 101343. doi:10.1016/j.phycom.2021.101343 ISSN 1874-4907.

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Official URL: http://dx.doi.org/10.1016/j.phycom.2021.101343

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Abstract

Signal detection using deep learning is a challenging and promising research topic. Several learning-based signal detectors have been proposed to produce significant results. However, most of them have ignored interference in their designs. In this paper, we evaluate the performance of learning-based signal detectors in the presence of co-channel interference under different channel conditions. Specifically, fully connected deep neural network (FCDNN) and convolutional neural network (CNN) are examined as the data-driven signal detector for blind signal detection without knowledge of the channel state information. Several important system parameters, including signal-to-interference ratio, number of interferences and type of interference, are considered. Numerical results show that FCDNN and CNN-based detectors have better performance and robustness to different SIRs conditions than traditional detectors in the presence of interference and FCDNN performs better than CNN when SIR is small and the order of interference modulation is high.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Signal detection , MIMO systems, Neural networks (Computer science) , Machine learning
Journal or Publication Title: Physical Communication
Publisher: Elsevier BV
ISSN: 1874-4907
Official Date: August 2021
Dates:
DateEvent
August 2021Published
19 April 2021Available
15 April 2021Accepted
Volume: 47
Article Number: 101343
DOI: 10.1016/j.phycom.2021.101343
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 22 April 2021
Date of first compliant Open Access: 19 April 2022
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
61873119[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61911530247[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809

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