Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In...Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.展开更多
Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin c...Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.展开更多
调制方式自动识别的目的就是在尽可能少的先验知识前提下,判断出通信信号的调制方式,并估计出相应的调制参数。近年来,许多学术和军事研究机构都将兴趣集中在调制识别算法的研究和开发上。通过对Nandi A K和Azzouz E E所提出特征值的介...调制方式自动识别的目的就是在尽可能少的先验知识前提下,判断出通信信号的调制方式,并估计出相应的调制参数。近年来,许多学术和军事研究机构都将兴趣集中在调制识别算法的研究和开发上。通过对Nandi A K和Azzouz E E所提出特征值的介绍、以及利用特征值对信号进行模拟信号调制方式识别的原理,提出了改进的特征值来取代传统特征值。仿真结果显示,改进的基于决策论的分类方法不但具有较高的识别率,而且相比改进前的分类器具有更快的识别速度。展开更多
文摘Although modulation classification based on deep neural network can achieve high Modulation Classification(MC)accuracies,catastrophic forgetting will occur when the neural network model continues to learn new tasks.In this paper,we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC.We innovate a research algorithm for continuous automatic MC.Firstly,a memory for storing representative old task modulation signals is built,which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend.Secondly,in order to better simulate the dynamic wireless communication environment,we employ the mini-batch gradient algorithm which is more suitable for continuous learning.Finally,the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model.Simulation results verify the effectiveness of the method.
文摘Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.
文摘调制方式自动识别的目的就是在尽可能少的先验知识前提下,判断出通信信号的调制方式,并估计出相应的调制参数。近年来,许多学术和军事研究机构都将兴趣集中在调制识别算法的研究和开发上。通过对Nandi A K和Azzouz E E所提出特征值的介绍、以及利用特征值对信号进行模拟信号调制方式识别的原理,提出了改进的特征值来取代传统特征值。仿真结果显示,改进的基于决策论的分类方法不但具有较高的识别率,而且相比改进前的分类器具有更快的识别速度。
