A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification
This paper presents a novel hybrid extreme learning machine (ELM) with cuckoo search algorithm (CSA) for the classification purposes of the digitally modulated signals, such as phase shift keying (PSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). Nine modulation schemes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8754798/ |
| _version_ | 1818323524967727104 |
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| author | Syed Ihtesham Hussain Shah Sheraz Alam Sajjad A. Ghauri Asad Hussain Faraz Ahmed Ansari |
| author_facet | Syed Ihtesham Hussain Shah Sheraz Alam Sajjad A. Ghauri Asad Hussain Faraz Ahmed Ansari |
| author_sort | Syed Ihtesham Hussain Shah |
| collection | DOAJ |
| description | This paper presents a novel hybrid extreme learning machine (ELM) with cuckoo search algorithm (CSA) for the classification purposes of the digitally modulated signals, such as phase shift keying (PSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). Nine modulation schemes having different orders have been considered for this paper. First, the Gabor filter is used to extract the key features from the received signal which are then optimized by the CSA. Finally, the ELM is used to classify the modulation schemes using these optimized features. Our proposed CSA-ELM approach is not only fast convergent and robust but also manifests improved percentage classification accuracy at low SNRs and lower sample size for both AWGN and Rayleigh fading channels. |
| first_indexed | 2024-12-13T11:14:04Z |
| format | Article |
| id | doaj.art-3a01b1e8c77043f88445978bf3aa87a3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:14:04Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a01b1e8c77043f88445978bf3aa87a32022-12-21T23:48:39ZengIEEEIEEE Access2169-35362019-01-017905259053710.1109/ACCESS.2019.29266158754798A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation ClassificationSyed Ihtesham Hussain Shah0Sheraz Alam1https://orcid.org/0000-0003-3498-475XSajjad A. Ghauri2Asad Hussain3https://orcid.org/0000-0003-3216-8118Faraz Ahmed Ansari4Department of Electrical Engineering, National University of Modern Languages, Islamabad, PakistanDepartment of Electrical Engineering, National University of Modern Languages, Islamabad, PakistanDepartment of Electrical Engineering, ISRA University, Islamabad, PakistanDepartment of Electrical Engineering, National University of Modern Languages, Islamabad, PakistanDepartment of Electrical Engineering, National University of Modern Languages, Islamabad, PakistanThis paper presents a novel hybrid extreme learning machine (ELM) with cuckoo search algorithm (CSA) for the classification purposes of the digitally modulated signals, such as phase shift keying (PSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). Nine modulation schemes having different orders have been considered for this paper. First, the Gabor filter is used to extract the key features from the received signal which are then optimized by the CSA. Finally, the ELM is used to classify the modulation schemes using these optimized features. Our proposed CSA-ELM approach is not only fast convergent and robust but also manifests improved percentage classification accuracy at low SNRs and lower sample size for both AWGN and Rayleigh fading channels.https://ieeexplore.ieee.org/document/8754798/Extreme learning machinecuckoo search algorithmGabor Featuresautomatic modulation recognition |
| spellingShingle | Syed Ihtesham Hussain Shah Sheraz Alam Sajjad A. Ghauri Asad Hussain Faraz Ahmed Ansari A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification IEEE Access Extreme learning machine cuckoo search algorithm Gabor Features automatic modulation recognition |
| title | A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification |
| title_full | A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification |
| title_fullStr | A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification |
| title_full_unstemmed | A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification |
| title_short | A Novel Hybrid Cuckoo Search- Extreme Learning Machine Approach for Modulation Classification |
| title_sort | novel hybrid cuckoo search extreme learning machine approach for modulation classification |
| topic | Extreme learning machine cuckoo search algorithm Gabor Features automatic modulation recognition |
| url | https://ieeexplore.ieee.org/document/8754798/ |
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