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

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.

Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network

“…The number of channels is not dilated in the first two-dimensional convolution (Conv2D) ensuring that the output feature sizes are the same as standard convolutions. Standard convolution processes spatial information and completes the dilation of channel information in the first Conv2D, which are calculated according to formulas (11) and ( 12) as follows:…”

“…[9]- [10] The authors use the adaptive weight myriad filter to preprocess the impulse noise in the received signal and propose an evolutionary neural network based on the quantum elephant herding algorithm (QEHA) to classify the modulated signal. [11] These methods usually have a good classification effect in single-in single-out (SISO) systems [12]- [13], but the space-time aliasing caused by the MIMO channel seriously degrades the effectiveness of the classical methods. Therefore, the traditional pattern recognition method can no longer adequately meet the needs of modulation recognition in the MIMO systems.…”