基于高階累積量和循環(huán)譜的信號調(diào)制方式混合識別算法

趙雄文; 郭春霞; 李景春

doi:10.11999/JEIT150747

基于高階累積量和循環(huán)譜的信號調(diào)制方式混合識別算法

doi: 10.11999/JEIT150747 cstr: 32379.14.JEIT150747

1.
(華北電力大學(xué)電氣與電子工程學(xué)院北京 102206) ②(國家無線電監(jiān)測中心北京 100037)

基金項(xiàng)目:

國家自然科學(xué)基金(61372051)

計(jì)量
- 文章訪問數(shù): 2114
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- 被引次數(shù): 0
出版歷程
- 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: 為了識別當(dāng)前通信系統(tǒng)所采用的主要調(diào)制方式，該文結(jié)合高階累積量和循環(huán)譜的特點(diǎn)，采用混合識別算法，同時(shí)應(yīng)用智能決策算法(神經(jīng)網(wǎng)絡(luò))對信號進(jìn)行識別。該算法基于四階和六階高階累積量構(gòu)造出一個(gè)新的特征參數(shù)，將數(shù)字調(diào)制信號分為{BPSK, 2ASK}, {QPSK}, {2FSK, 4FSK}, {MSK}和{16QAM, 64QAM}5類。然后利用高階累積量的其它特征參數(shù)以及循環(huán)譜特征對{OFDM}, {16QAM, 64QAM}, {2ASK, BPSK}及{2FSK, 4FSK}進(jìn)行識別。為便于工程實(shí)現(xiàn)，該文采用半實(shí)物仿真以及LabVIEW和MATLAB混合編程來驗(yàn)證算法。仿真結(jié)果證明，該算法能夠在較低信噪比下實(shí)現(xiàn)對{OFDM, BPSK, QPSK, 2ASK, 2FSK, 4FSK, MSK, 16QAM, 64QAM}等多種信號的分類，在信噪比高于 5 dB時(shí)，調(diào)制方式識別率可達(dá)94%以上，由此證明了該方法的有效性。
- 調(diào)制識別 /
- 高階累積量 /
- 循環(huán)譜 /
- 神經(jīng)網(wǎng)絡(luò)
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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參考文獻(xiàn)(25)

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