research-article

Digital signal modulation recognition method based on high-order cumulants and wavelet transform

Authors:

Peiru LiuAuthors Info & Claims

ACAI '20: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 89, Pages 1 - 6

https://doi.org/10.1145/3446132.3446423

Published: 09 March 2021 Publication History

Abstract

In view of the current situation that the recognition rate of digital signal modulation recognition method is unsatisfactory at low Signal-to-Noise Ratio(SNR), a recognition method based on high-order cumulants and wavelet transform is proposed to realize the automatic modulation recognition of 8 kinds of digital signals such as 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM and 32QAM. Based on the high-order cumulants principle and wavelet transform theory, the characteristic parameters f1∼f5 are constructed by the elaborate analysis of the characteristic extraction of these signals. Through simulation experiments, the characteristic parameter changes of different types of modulation signals at different SNR are obtained, and design the classifier of Back Propagation (BP) neural network to classify the signals. The simulation results show that this method can improve the average correct recognition rates of 8 digital modulation signals reaching up to above 97% when the SNR is higher than 0dB, which greatly improves the signal recognition performance at low SNR.

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Cited By

Hong HXia J(2023)Intelligent Identification of Modulation Patterns Based on the GA-BP Neural Network2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA59209.2023.10257823(1695-1699)Online publication date: 11-Aug-2023
https://doi.org/10.1109/ICIPCA59209.2023.10257823

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ACAI '20: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

December 2020

576 pages

ISBN:9781450388115

DOI:10.1145/3446132

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 09 March 2021

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National Key Industry Innovation Chain Project of China

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ACAI 2020

ACAI 2020: 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

December 24 - 26, 2020

Sanya, China

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Overall Acceptance Rate 173 of 395 submissions, 44%

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Cited By

Hong HXia J(2023)Intelligent Identification of Modulation Patterns Based on the GA-BP Neural Network2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA59209.2023.10257823(1695-1699)Online publication date: 11-Aug-2023
https://doi.org/10.1109/ICIPCA59209.2023.10257823

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