Database Assisted Automatic Modulation Classification Using Sequential Minimal Optimization
In this paper, we have proposed a novel algorithm for identifying the modulation scheme of an unknown incoming signal in order to mitigate the interference with primary user in Cognitive Radio systems, which is facilitated by using Automatic Modulation Classification (AMC) at the front end of Softwa...
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| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
20-06-2018
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| Online Access: | Get full text |
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| Summary: | In this paper, we have proposed a novel algorithm for identifying the
modulation scheme of an unknown incoming signal in order to mitigate the
interference with primary user in Cognitive Radio systems, which is facilitated
by using Automatic Modulation Classification (AMC) at the front end of Software
Defined Radio (SDR). In this study, we used computer simulations of analog and
digital modulations belonging to eleven classes. Spectral based features have
been used as input features for Sequential Minimal Optimization (SMO). These
features of primary users are stored in the database, then it matches the
unknown signal's features with those in the database. Built upon recently
proposed AMC, our new database approach inherits the benefits of SMO based
approach and makes it much more time efficient in classifying an unknown
signal, especially in the case of multiple modulation schemes to overcome the
issue of intense computations in constructing features. In various
applications, primary users own frequent wireless transmissions having limited
their feature size and save few computations. The SMO based classification
methodology proves to be over 99 \% accurate for SNR of 15 dB and accuracy of
classification is over 95 \% for low SNRs around 5dB. |
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| DOI: | 10.48550/arxiv.1806.07566 |