基于高阶累积量和循环谱的信号调制方式混合识别算法

赵雄文; 郭春霞; 李景春

doi:10.11999/JEIT150747

基于高阶累积量和循环谱的信号调制方式混合识别算法

doi: 10.11999/JEIT150747

1.
(华北电力大学电气与电子工程学院北京 102206) ②(国家无线电监测中心北京 100037)

基金项目:

国家自然科学基金(61372051)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-06-18
- 修回日期: 2015-12-08
- 刊出日期: 2016-03-19

Mixed Recognition Algorithm for Signal Modulation Schemes by High-order Cumulants and Cyclic Spectrum

1.
(School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)
2.
(The State Radio Monitoring Center, Beijing 100037, China)

Funds:

The National Natural Science Foundation of China (61372051)

摘要

摘要: 为了识别当前通信系统所采用的主要调制方式，该文结合高阶累积量和循环谱的特点，采用混合识别算法，同时应用智能决策算法(神经网络)对信号进行识别。该算法基于四阶和六阶高阶累积量构造出一个新的特征参数，将数字调制信号分为{BPSK, 2ASK}, {QPSK}, {2FSK, 4FSK}, {MSK}和{16QAM, 64QAM}5类。然后利用高阶累积量的其它特征参数以及循环谱特征对{OFDM}, {16QAM, 64QAM}, {2ASK, BPSK}及{2FSK, 4FSK}进行识别。为便于工程实现，该文采用半实物仿真以及LabVIEW和MATLAB混合编程来验证算法。仿真结果证明，该算法能够在较低信噪比下实现对{OFDM, BPSK, QPSK, 2ASK, 2FSK, 4FSK, MSK, 16QAM, 64QAM}等多种信号的分类，在信噪比高于 5 dB时，调制方式识别率可达94%以上，由此证明了该方法的有效性。
- 调制识别 /
- 高阶累积量 /
- 循环谱 /
- 神经网络
Abstract: To recognize the major modulation schemes which are applied to concurrent communication systems, a joint method based on the high-order cumulants and cyclic spectrum with intelligent decision algorithm (neural network) is proposed to recognize the modulation schemes for digital signals. Firstly, a new featured parameter is extracted from the four-order and six-order cumulants of the digital signals to identify the modulation schemes of {BPSK, 2ASK}, {QPSK}, {2FSK, 4FSK}, {MSK}, and {16QAM, 64QAM}, then {OFDM}, {16QAM, 64QAM}, {2ASK, BPSK}, and {2FSK, 4FSK} are classified by the other featured parameters of the joint high-order cumulants and cyclic spectrum algorithms. In order to facilitate the engineering implementation, the semi-physical simulation and mixed programming of LabVIEW and MATLAB are used to validate the proposed algorithms. Simulation results show that the algorithms can recognize modulations {OFDM, BPSK, QPSK, 2ASK, 2FSK, 4FSK, MSK, 16QAM, 64QAM} with small Signal-to-Noise Ratio (SNR). The average recognition rate is more than 94% with SNR greater or equal than 5 dB, which validates the effectiveness of the proposed algorithms.
- Modulation recognition /
- High-order cumulants /
- Cyclic spectrum /
- Neural network

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参考文献(25)

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