Automatic Modulation Classification for CR Using Deep Learning

Automatic Modulation Classification holds significant importance in the realm of wireless communication systems. The proliferation of modulation schemes, coupled with the escalating complexity of channel environments, necessitates a heightened accuracy in discerning modulated signals. Traditional me...

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Veröffentlicht in:SN computer science Jg. 5; H. 8; S. 1061
Hauptverfasser: Solanki, Surendra, Brahma, Banalaxmi, Singh, Yadvendra Pratap
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.12.2024
Springer Nature B.V
Schlagworte:
Accuracy
Adaptation
Algorithms
Artificial neural networks
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Deep learning
Efficiency
Energy consumption
Feature selection
Fourier transforms
Information Systems and Communication Service
Literature reviews
Machine learning
Modulation
Neural networks
Original Research
Pattern Recognition and Graphics
Radios
Receivers & amplifiers
Research Advancements in Network Technologies
Satellite communications
Software Engineering/Programming and Operating Systems
Spectrum allocation
Vision
Wireless communication systems
Wireless communications
Modulation modes
Transfer learning
Convolutional neural network
Long short term memory
Classification
ISSN:2661-8907, 2662-995X, 2661-8907
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Automatic Modulation Classification holds significant importance in the realm of wireless communication systems. The proliferation of modulation schemes, coupled with the escalating complexity of channel environments, necessitates a heightened accuracy in discerning modulated signals. Traditional methods struggle to effectively classify this array of modulation schemes. In response, this paper advocates the employment of deep learning techniques for precise modulation classification. A deep learning architecture is introduced, comprising a convolutional neural network, and a long short-term memory neural network leveraging the power of transfer learning. The convolutional neural network adeptly extracts spatial features from the received signal, while a bidirectional long short-term memory neural network is engineered to capture temporal features. Transfer learning is employed for efficient parameter sharing. Empirical evaluations were conducted using the RadioML2016.10b dataset, affirming the efficacy of the proposed mechanism.
Bibliographie:ObjectType-Article-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-03410-2