Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels
Automatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learnin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9845419/ |
| _version_ | 1828187404814516224 |
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| author | P. Dileep Aashvi Singla Dibyajyoti Das Prabin Kumar Bora |
| author_facet | P. Dileep Aashvi Singla Dibyajyoti Das Prabin Kumar Bora |
| author_sort | P. Dileep |
| collection | DOAJ |
| description | Automatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learning (DL) has recently gained prominence among AMC researchers. It is shown that convolutional neural network based classifiers are very efficient in the AMC for both single input single output (SISO) and multiple-input multiple-output (MIMO) systems. However, for most of the works in MIMO-AMC, the channel considered is full rank. This work addresses the problem of AMC over rank deficient channels such as a keyhole channel using a DL-based classifier. The classifier utilizes a CNN, which does not employ pooling layers or dropouts in the convolutional layers. To further improve the classification accuracy, decision cooperation as well as feature fusion is employed. In addition to the keyhole effect, this work investigates the effect of antenna correlation on DL-based AMC. A comparative study of the proposed method and the existing FB AMC method for the MIMO keyhole channel is also presented. |
| first_indexed | 2024-04-12T07:34:58Z |
| format | Article |
| id | doaj.art-cc13f55bd1fa49ebabd3fa185fd93aca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T07:34:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cc13f55bd1fa49ebabd3fa185fd93aca2022-12-22T03:41:59ZengIEEEIEEE Access2169-35362022-01-011011956611957410.1109/ACCESS.2022.31952299845419Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole ChannelsP. Dileep0https://orcid.org/0000-0003-3926-8146Aashvi Singla1Dibyajyoti Das2Prabin Kumar Bora3IIT Guwahati, Guwahati, IndiaIIT Guwahati, Guwahati, IndiaIIT Guwahati, Guwahati, IndiaIIT Guwahati, Guwahati, IndiaAutomatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learning (DL) has recently gained prominence among AMC researchers. It is shown that convolutional neural network based classifiers are very efficient in the AMC for both single input single output (SISO) and multiple-input multiple-output (MIMO) systems. However, for most of the works in MIMO-AMC, the channel considered is full rank. This work addresses the problem of AMC over rank deficient channels such as a keyhole channel using a DL-based classifier. The classifier utilizes a CNN, which does not employ pooling layers or dropouts in the convolutional layers. To further improve the classification accuracy, decision cooperation as well as feature fusion is employed. In addition to the keyhole effect, this work investigates the effect of antenna correlation on DL-based AMC. A comparative study of the proposed method and the existing FB AMC method for the MIMO keyhole channel is also presented.https://ieeexplore.ieee.org/document/9845419/Automatic modulation classification (AMC)deep learningconvolutional neural network (CNN)keyhole channelmultiple input multiple output systems (MIMO)correlated MIMO channels |
| spellingShingle | P. Dileep Aashvi Singla Dibyajyoti Das Prabin Kumar Bora Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels IEEE Access Automatic modulation classification (AMC) deep learning convolutional neural network (CNN) keyhole channel multiple input multiple output systems (MIMO) correlated MIMO channels |
| title | Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels |
| title_full | Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels |
| title_fullStr | Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels |
| title_full_unstemmed | Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels |
| title_short | Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels |
| title_sort | deep learning based automatic modulation classification over mimo keyhole channels |
| topic | Automatic modulation classification (AMC) deep learning convolutional neural network (CNN) keyhole channel multiple input multiple output systems (MIMO) correlated MIMO channels |
| url | https://ieeexplore.ieee.org/document/9845419/ |
| work_keys_str_mv | AT pdileep deeplearningbasedautomaticmodulationclassificationovermimokeyholechannels AT aashvisingla deeplearningbasedautomaticmodulationclassificationovermimokeyholechannels AT dibyajyotidas deeplearningbasedautomaticmodulationclassificationovermimokeyholechannels AT prabinkumarbora deeplearningbasedautomaticmodulationclassificationovermimokeyholechannels |