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Modified receiver architecture in software-defined radio for real-time modulation classification

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Published in:EURASIP Journal on Advances in Signal Processing vol. 2024, no. 1 (Dec 2024), p. 77
Main Author: Le, Quoc Nam
Other Authors: Huynh, Tan Quoc, Ta, Hien Quang, Tan, Phuoc Vo, Nguyen, Lap Luat
Published:
Springer Nature B.V.
Subjects:
Software reliability
Cognitive radio
Communications systems
Classification
Computer architecture
Machine learning
Real time
Modulation
Artificial neural networks
Software radio
Demodulation
Receivers & amplifiers
Simulation
Software
Accuracy
Deep learning
Memory
Neural networks
Signal processing
Adaptation
Performance evaluation
Online Access:Citation/Abstract
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Abstract: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.
ISSN:1687-6172
1687-6180
DOI:10.1186/s13634-024-01176-6
Source:Advanced Technologies & Aerospace Database