Satellite Amplitude-Phase Signals Modulation Identification and Demodulation Algorithm Based on the Cyclic Neural Network

被引：0
|
作者：
Zha X. [1 ]
Peng H. [1 ]
Qin X. [1 ]
Li T.-Y. [1 ]
Li G. [1 ]
机构：
[1] PLA Strategic Support Force Information Engineering University, Zhengzhou, 450001, Henan
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 11期
关键词：
Cyclic neural network; Intelligent processing; Modulation recognition; Signal demodulation;
D O I：
10.3969/j.issn.0372-2112.2019.11.029
中图分类号：
学科分类号：
摘要：
A cognitive signal recognition and demodulation model is designed based on the cyclic neural network for conventional amplitude-phase satellite modulations.Through the cyclic neural unit, the features of the target signal are extracted.And the features are dimension-mapped by the fully connected neural network.The model finally completes the modulation recognition and demodulation of the target signal with these mapped features.This method does not need much prior knowledge about signal-to-noise ratio (S/N), and it is not sensitive to frequency offset.The method also has good adaptability in the maintenance and extension, which conforms to the demands of the engineering, while the traditional algorithms need to redeploy the decision rule.Computer simulations show that the correct recognition probability is close to 98% when S/N is greater than 6 dB and demodulation error rate is close to the theoretical gate.The presented theoretical form provides a new idea for intelligent signal processing, and it can also be used in other communication signal processing fields. © 2019, Chinese Institute of Electronics. All right reserved.
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页码：2443 / 2448
页数：5
相关论文
共 9 条
  • [1] Zhang J.-L., Wang B., Wang Y., Liu M.-Q., Analgorithm for recognition and parameters estimation of OFDM in alpha stable distribution noise, Acta Electronica Sinica, 46, 6, pp. 1390-1396, (2018)
  • [2] Liao C.-H., Tu S.-L., Wan J., An anti-frequency-offset algorithm for modulation recognition of satellite amplitude-phase modulated signals, Journal of Electronics & Information Technology, 36, 2, pp. 346-352, (2014)
  • [3] Meng X.-B., Bao C.-C., Noiseestimate algorithm based on minima controlled GARCH model, Acta Electronica Sinica, 44, 3, pp. 747-752, (2016)
  • [4] O'Shea T.J., Corgan J., Clancy T.C., Unsupervised representation learning of structured radio communication signals, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines, pp. 1-5, (2016)
  • [5] O'Shea T.J., Karra K., Clancy T.C., Learning tocommunicate: channel auto-encoders, domain specific regularizes, and attention, 2016 IEEE International Symposium on Signal Processing and Information Technology, pp. 223-228, (2016)
  • [6] ETSI TR 102376 v1.1.1, Digital Video Broadcasting (DVB) User Guidelines for the Second Generation System for Broadcasting, Interactive Services, News Gathering and Other Broadband Satellite Applications (DVB-S2)
  • [7] Yang L., He X.-D., Ding C.-W., Compensationmethod for nonlinear distortion based on clipping and compressive sensing in OFDM system, Acta Electronica Sinica, 44, 9, pp. 2051-2056, (2016)
  • [8] Hochreiter S., Schmidhuber J., Long short-term memory, Neural Computation, 9, 8, pp. 1735-1780, (1997)
  • [9] Vincent P., Larochelle H., Lajoie I., Et al., Stackeddenoising autoencoders: learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, 11, 12, pp. 3371-3408, (2010)
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