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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
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Official URL: http://dx.doi.org/10.1016/j.phycom.2021.101343
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 | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > 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: |
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| 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 | |||||||||
| RIOXX Funder/Project Grant: |
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