Deep learning for communication signal classification
The ability to differentiate between different radio signals is important when using communication signals. This is achieved with modulation classification. In this study, the use of two deep learning approaches, the convolutional neural network and the gated recurrent unit for modulation class...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157988 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The ability to differentiate between different radio signals is important when using
communication signals. This is achieved with modulation classification.
In this study, the use of two deep learning approaches, the convolutional neural
network and the gated recurrent unit for modulation classification of five different
signal modulations schemes, Gaussian Frequency Shift Keying, Binary Phase Shift
Keying, Broadcast Frequency Modulation, Double Sideband Amplitude Modulation
and Single Sideband Amplitude Modulation are explored.
The network performances are compared based on the classification accuracy of
various test sets. Each network variation was trained with a range of signal sample
sizes and the signal modulation classification accuracy evaluated for each network
and sample variation. The overall test results for both networks indicate that the
convolutional neural network outperforms the gated recurrent unit albeit by a small
margin of 4% to 8%.
The outcome of the study shows the exceptional potential for deep learning
approaches in communication signal classification as seen from the promising results
displayed by both networks evaluated in this study |
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