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

ZHAO Xiongwen; GUO Chunxia; LI Jingchun

doi:10.11999/JEIT150747

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 674-680

ZHAO Xiongwen, GUO Chunxia, LI Jingchun. Mixed Recognition Algorithm for Signal Modulation Schemes by High-order Cumulants and Cyclic Spectrum[J]. Journal of Electronics & Information Technology, 2016, 38(3): 674-680. doi: 10.11999/JEIT150747

Citation:

ZHAO Xiongwen, GUO Chunxia, LI Jingchun. Mixed Recognition Algorithm for Signal Modulation Schemes by High-order Cumulants and Cyclic Spectrum[J]. Journal of Electronics & Information Technology, 2016, 38(3): 674-680. doi: 10.11999/JEIT150747

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

doi: 10.11999/JEIT150747

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)

Received Date: 2015-06-18
Rev Recd Date: 2015-12-08
Publish Date: 2016-03-19

Abstract

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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References(25)

References

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