• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms

Vol. 14, No. 12, December 31, 2020
10.3837/tiis.2020.12.003, Download Paper (Free):

Abstract

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Q. Jing, H. Wang and L. Yang, "Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms," KSII Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4664-4681, 2020. DOI: 10.3837/tiis.2020.12.003.

[ACM Style]
Qingfeng Jing, Huaxia Wang, and Liming Yang. 2020. Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms. KSII Transactions on Internet and Information Systems, 14, 12, (2020), 4664-4681. DOI: 10.3837/tiis.2020.12.003.