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http://dx.doi.org/10.3837/tiis.2021.07.004

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise  

Gao, Hongyuan (College of Information and Communication Engineering, Harbin Engineering University)
Wang, Shihao (College of Information and Communication Engineering, Harbin Engineering University)
Su, Yumeng (College of Information and Communication Engineering, Harbin Engineering University)
Sun, Helin (College of Information and Communication Engineering, Harbin Engineering University)
Zhang, Zhiwei (College of Information and Communication Engineering, Harbin Engineering University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.7, 2021 , pp. 2356-2376 More about this Journal
Abstract
In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.
Keywords
impulsive noise; adaptive weight myriad filter; instantaneous characteristics; high order cumulants; quantum elephant herding algorithm; BP neural network;
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