Modified receiver architecture in software-defined radio for real-time modulation classification
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| Publicado en: | EURASIP Journal on Advances in Signal Processing vol. 2024, no. 1 (Dec 2024), p. 77 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1186/s13634-024-01176-6 |2 doi | |
| 035 | |a 3081501474 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 130316 |2 nlm | ||
| 100 | 1 | |a Le, Quoc Nam |u International University, School of Electrical Engineering, Ho Chi Minh city, Vietnam (GRID:grid.440795.b) (ISNI:0000 0004 0493 5452); Vietnam National University, Ho Chi Minh city, Vietnam (GRID:grid.444808.4) (ISNI:0000 0001 2037 434X) | |
| 245 | 1 | |a Modified receiver architecture in software-defined radio for real-time modulation classification | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications. | |
| 653 | |a Software reliability | ||
| 653 | |a Cognitive radio | ||
| 653 | |a Communications systems | ||
| 653 | |a Classification | ||
| 653 | |a Computer architecture | ||
| 653 | |a Machine learning | ||
| 653 | |a Real time | ||
| 653 | |a Modulation | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Software radio | ||
| 653 | |a Demodulation | ||
| 653 | |a Receivers & amplifiers | ||
| 653 | |a Simulation | ||
| 653 | |a Software | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Memory | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Adaptation | ||
| 653 | |a Performance evaluation | ||
| 700 | 1 | |a Huynh, Tan Quoc |u International University, School of Electrical Engineering, Ho Chi Minh city, Vietnam (GRID:grid.440795.b) (ISNI:0000 0004 0493 5452); Vietnam National University, Ho Chi Minh city, Vietnam (GRID:grid.444808.4) (ISNI:0000 0001 2037 434X) | |
| 700 | 1 | |a Ta, Hien Quang |u International University, School of Electrical Engineering, Ho Chi Minh city, Vietnam (GRID:grid.440795.b) (ISNI:0000 0004 0493 5452); Vietnam National University, Ho Chi Minh city, Vietnam (GRID:grid.444808.4) (ISNI:0000 0001 2037 434X) | |
| 700 | 1 | |a Tan, Phuoc Vo |u International University, School of Electrical Engineering, Ho Chi Minh city, Vietnam (GRID:grid.440795.b) (ISNI:0000 0004 0493 5452); Vietnam National University, Ho Chi Minh city, Vietnam (GRID:grid.444808.4) (ISNI:0000 0001 2037 434X) | |
| 700 | 1 | |a Nguyen, Lap Luat |u International University, School of Electrical Engineering, Ho Chi Minh city, Vietnam (GRID:grid.440795.b) (ISNI:0000 0004 0493 5452); Vietnam National University, Ho Chi Minh city, Vietnam (GRID:grid.444808.4) (ISNI:0000 0001 2037 434X) | |
| 773 | 0 | |t EURASIP Journal on Advances in Signal Processing |g vol. 2024, no. 1 (Dec 2024), p. 77 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3081501474/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3081501474/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |