接头(建筑物)
计算机科学
深度学习
人工智能
光谱(功能分析)
计算机体系结构
语音识别
工程类
物理
建筑工程
量子力学
作者
O Nesraoui,Djamal Teguig,Saïd Sadoudi
出处
期刊:Physica Scripta
[IOP Publishing]
日期:2024-04-02
卷期号:99 (5): 056008-056008
标识
DOI:10.1088/1402-4896/ad395b
摘要
Abstract Automatic modulation classification (AMC) aims to blindly recognize the modulation type of a received signal in wireless systems. It is also a critical component of non-cooperative communication systems after the detection of the presence of a signal. In this paper, we introduce a robust approach, termed DET-AMC (joint Detection and Automatic Modulation Classification), employing Convolutional Neural Networks (CNNs) trained via transfer learning methodology. The main advantage of our approach is its ability to handle a wide range of modulation types, including 10 different schemes generated in Gnuradio and their detection using the same model. Through extensive experimentation, we evaluate the performance of our light CNN-based DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well as in the presence of phase noise and frequency offset. We find that the CNN’s learned features, obtained through transfer learning, exhibit robustness, particularly in low SNR and various challenging conditions, leading to accurate modulation classification. In general, our approach outperforms existing methods by using the effectiveness of deep learning in capturing relevant discriminative features. Additionally, our model offers a robust solution for join detection and AMC by achieving an accurate probability of detection and modulation classification without the need for manual feature engineering or the consideration of frequency offset, phase noise or noise estimation. Our model achieves 100% accuracy for synthetic and real data at an SNR equal to -10 dB for detection, and 100% and 98% for classification of synthetic and real signals at −4 dB, respectively.
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