Modified receiver architecture in software-defined radio for real-time modulation classification
保存先:
| 出版年: | EURASIP Journal on Advances in Signal Processing vol. 2024, no. 1 (Dec 2024), p. 77 |
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| 第一著者: | |
| その他の著者: | , , , |
| 出版事項: |
Springer Nature B.V.
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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| 抄録: | 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. |
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| ISSN: | 1687-6172 1687-6180 |
| DOI: | 10.1186/s13634-024-01176-6 |
| ソース: | Advanced Technologies & Aerospace Database |