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- Title
基于多特征 SSA-ELM 的调制识别.
- Authors
肖 潇; 谢跃雷
- Abstract
In order to solve the problems of low recognition rate under low signal-to-noise ratio(SNR),slow training speed and few recognition types of modulation in current modulation recognition algorithms, a modulation recognition algorithm based on multiple features and Sparrow Search Algorithm-Extreme Learning Machine(SSA-ELM) is proposed. The algorithm constructs two characteristic parameters based on fourth-order and sixth-order cumulants, and introduces a new time-frequency tool called Fractional Wavelet Transform, which uses fractional domain wavelet coefficients to construct eigenvalues and form three dimensional eigenvectors, then uses the SSA-ELM network for classification. Verification result by simulation and Universal Software Radio Peripheral(USRP) collected data shows that the proposed feature parameters have good robustness, and the classification performance of the ELM network optimized by the SSA algorithm has been significantly improved. When the SNR is 6 dB, the recognition rate of both algorithms is 90%,and the highest recognition rate reaches 94% .
- Subjects
MACHINE learning; SOFTWARE radio; WAVELET transforms; SIGNAL-to-noise ratio; SIMULATION software; EIGENVALUES; EIGENVECTORS; CLASSIFICATION algorithms
- Publication
Telecommunication Engineering, 2022, Vol 62, Issue 8, p1044
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2022.08.004