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Convolutional neural networks(CNNs) are the ways to determine the received signal without having any channel parameters and any prior proficiency of the incoming signal. A synthetic channel impairment waveform is generated. Using the generated waveform as training data and training the CNN for classifying the modulation. The CNN can also be tested with software-defined radio hardware and over-the-air signals and gives high accuracy than the traditional method. The proposed architecture performs six-layer convolution to the incoming signal and delivers around 95% of test accuracy with 30dB SNR, subjected to Racian multi-path fading, and also uses multiple modulation schemes for the classification at 30dB SNR.
Keywords:
Signal generation, synthetic channel, CNN algorithm, classification, accuracy
Cite Article:
"Received Signal Classification of Modulation Scheme at Receiver using CNN Algorithm", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 10, page no.760 - 764, October-2022, Available :http://www.ijrti.org/papers/IJRTI2210103.pdf
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000205082
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator