为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对...为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对A/P模态数据进一步处理,更有效地学习数据间的重复特征,避免信息冗余.模型中引入双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM),利用其双向时序特征提取能力,增强模型对复杂时序信息的理解.实验结果表明,所提模型在数据集RadioML2016.10A上表现良好.当SNR低于−8 dB时,平均识别精度比主流模型提升6%,而SNR在0–18 dB时,平均识别精度比主流模型提高2%–10%,且在SNR为16 dB时,识别精度高达94.32%.另外,将模型迁移到数据集RadioML2016.10B所得结果同样最优,且当SNR为18 dB时识别精度高达93.91%.展开更多
In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robus...In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.展开更多
文摘为提升调制识别的准确性和鲁棒性,本文提出了一种全新的双模态混合调制识别模型.模型同时考虑原始时域同相正交(in-phase and quadrature,I/Q)和幅度相位(amplitude and phase,A/P)双模态数据以探索信号的时空相关性.采用双路对称结构对A/P模态数据进一步处理,更有效地学习数据间的重复特征,避免信息冗余.模型中引入双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM),利用其双向时序特征提取能力,增强模型对复杂时序信息的理解.实验结果表明,所提模型在数据集RadioML2016.10A上表现良好.当SNR低于−8 dB时,平均识别精度比主流模型提升6%,而SNR在0–18 dB时,平均识别精度比主流模型提高2%–10%,且在SNR为16 dB时,识别精度高达94.32%.另外,将模型迁移到数据集RadioML2016.10B所得结果同样最优,且当SNR为18 dB时识别精度高达93.91%.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1063319).
文摘In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.
