Modulation recognition based on the two-dimensional asynchronous in-phase quadrature histogram

被引：0

作者：

Wan, Pengmu ^{[1
]}

Hui, Xi ^{[1
]}

Chen, Dongrui ^{[1
]}

Wu, Bo ^{[2
]}

机构：

[1] School of Communications and Information Engineering, Xi' An University of Posts and Telecommunications, Xi'an,710121, China

[2] SUNNY SCIENCE & TECHNOLOGY CO., LTD, Xi'an,710075, China

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2024年 / 51卷 / 04期

关键词：

Automatic modulation recognition technology accurately identifies the modulation type of signals; making it a key technology in the field of signal processing. Traditional recognition methods suffer from low accuracy at low signal-to-noise ratios; and performance degradation or failure when dealing with signal frequency instability or asynchronous sampling. In this paper; we investigate modulation recognition technology based on deep learning for low-speed asynchronous sampled signals under channel conditions with varying signal-to-noise ratios and delays. We start by modeling low-speed asynchronous sampled signals and generating a two-dimensional asynchronous in-phase quadrature histogram using their in-phase and quadrature components. Subsequently; we employ a Radial Basis Function Neural Network to extract feature parameters from this two-dimensional image; thus achieving modulation type recognition for the input signal. Extensive computer simulations validate the proposed method' s accuracy in recognizing seven modulation types under the influence of additive white Gaussian noise. Experimental results demonstrate that; in the presence of additive white Gaussian noise in the channel model and with an input signal-to-noise ratio of 6 dB; the average recognition accuracy can exceed 95%. Comparative experiments further verify the effectiveness and robustness of the proposed approach. © 2024 Science Press. All rights reserved;

D O I：

10.19665/j.issn1001-2400.20240312

中图分类号：

学科分类号：

摘要：

引用

页码：78 / 90

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