scholarly journals Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jibo Shi ◽  
Lin Qi ◽  
Kuixian Li ◽  
Yun Lin
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Recognition Rate ◽  
Learning Approaches ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Recognition Method ◽  
Privacy And Security ◽  
Spectrum Detection

Signal modulation recognition is widely utilized in the field of spectrum detection, channel estimation, and interference recognition. With the development of artificial intelligence, substantial advances in signal recognition utilizing deep learning approaches have been achieved. However, a huge amount of data is required for deep learning. With increasing focus on privacy and security, barriers between data sources are sometimes difficult to break. This limits the data and renders them weak, so that deep learning is not sufficient. Federated learning can be a viable way of solving this challenge. In this article, we will examine the recognition of signal modulation based on federated learning with differential privacy, and the results show that the recognition rate is acceptable while data protection and security are being met.

scholarly journals Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42841-42847 ◽  
Author(s):  
Jie Shi ◽  
Sheng Hong ◽  
Changxin Cai ◽  
Yu Wang ◽  
Hao Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Phase Offset ◽  
Automatic Modulation Recognition

scholarly journals Research on Speech Recognition Method in Multi Layer Perceptual Network Environment

2021 ◽  
Vol 15 ◽  
pp. 996-1004
Author(s):  
Kai Zhao ◽  
Dan Wang
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Average Energy ◽  
Signal Recognition ◽  
Network Environment ◽  
Recognition Method ◽  
Zero Crossing ◽  
Zero Crossing Rate

Aiming at the problem of low recognition rate in speech recognition methods, a speech recognition method in multi-layer perceptual network environment is proposed. In the multi-layer perceptual network environment, the speech signal is processed in the filter by using the transfer function of the filter. According to the framing process, the speech signal is windowed and framing processed to remove the silence segment of the speech signal. At the same time, the average energy of the speech signal is calculated and the zero crossing rate is calculated to extract the characteristics of the speech signal. By analyzing the principle of speech signal recognition, the process of speech recognition is designed, and the speech recognition in multi-layer perceptual network environment is realized. The experimental results show that the speech recognition method designed in this paper has good speech recognition performance

scholarly journals A Survey on Deep Learning Techniques in Wireless Signal Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaofan Li ◽  
Fangwei Dong ◽  
Sha Zhang ◽  
Weibin Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Future Trends ◽  
Signal Recognition ◽  
Wireless Technology ◽  
Modulation Recognition ◽  
Spectrum Monitoring ◽  
Learning Techniques ◽  
Wireless Signal ◽  
New Challenges

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

scholarly journals A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 1166 ◽  
Author(s):  
Kaiyuan Jiang ◽  
Jiawei Zhang ◽  
Haibin Wu ◽  
Aili Wang ◽  
Yuji Iwahori
Keyword(s):  
Phase Shift ◽  
Rapid Development ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Phase Shift Keying ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Shift Keying

The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR.

scholarly journals An Accurate Modulation Recognition Method of QPSK Signal

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yongxin Feng ◽  
Zhenyu Teng ◽  
Fanwei Meng ◽  
Bo Qian
Keyword(s):  
Communication Systems ◽  
Signal Classification ◽  
Signal Recognition ◽  
Phase Shift Keying ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Space Condition ◽  
Quadrature Phase ◽  
Shift Keying ◽  
Simulation Results

Quadrature phase shift keying (QPSK) modulation has been widely applied in communication systems. With the increasing development of QPSK modulation recognition, it is meaningful for ensuring validity and accuracy of recognition method. Therefore, the method of signal recognition has been presented based on the features of QPSK modulated signal, in which the feature parameters of QPSK signal are extracted. Besides, signal classification is fulfilled through thresholds, and modulation recognition is completed with quartic spectrum. The simulation results show that, the method can recognize QPSK modulation effectively in less sample space condition and can be more accurate.

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