A novel modulation classification approach using Gabor filter network.
A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWG...
| Published in: | Scientific World Journal pp. 643671 - 643672 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109675451&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109675451 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109675451 NLM25126603 2012685014 10.1155/2014/643671 NLM25126603 PMC4122807 109675451 ppf: 643671 ppct: 1 formats: tig: atl: A novel modulation classification approach using Gabor filter network. aug: au: Ghauri, Sajjad Ahmed Qureshi, Ijaz Mansoor Cheema, Tanveer Ahmed Malik, Aqdas Naveed sug: ab: A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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