Deep learning-based robust algorithm for automatic modulation recognition
The project introduced a Deep Learning-based Automatic Modulation Recognition (DL- AMR) program employing various neural network architectures (CNN, ResNet, and LSTM) to enhance signal processing in 5G communication systems. It aimed to autonomously recognise modulation types of signals without prio...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/176262 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The project introduced a Deep Learning-based Automatic Modulation Recognition (DL- AMR) program employing various neural network architectures (CNN, ResNet, and LSTM) to enhance signal processing in 5G communication systems. It aimed to autonomously recognise modulation types of signals without prior knowledge, a task traditionally performed with manual feature engineering. The performance of these neural networks was compared by analysing accuracy in relation to signal-to-noise ratio (SNR). The discussion revolved around the classification accuracy of different modulation schemes at various SNRs, leading to the identification of the most effective DL-AMR model. Recommendations on future research in this area were highlighted finally. |
|---|