Differentiable architecture search-based automatic modulation recognition for multi-carrier signals

Considering the lack of a general multi-carrier signal dataset in urban multipath channels, and the challenge of recognizing the modulation types of distorted signals at low signal-to-noise ratio (SNR), a differentiable architecture search-based (DARTS) automatic modulation recognition algorithm for...

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Main Authors: LI Jie, LI Jing, LYU Lu, GONG Fengkui
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2024-09-01
Series:Tongxin xuebao
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Online Access:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024164/
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author LI Jie
LI Jing
LYU Lu
GONG Fengkui
author_facet LI Jie
LI Jing
LYU Lu
GONG Fengkui
author_sort LI Jie
collection DOAJ
description Considering the lack of a general multi-carrier signal dataset in urban multipath channels, and the challenge of recognizing the modulation types of distorted signals at low signal-to-noise ratio (SNR), a differentiable architecture search-based (DARTS) automatic modulation recognition algorithm for multi-carrier signals was proposed. Firstly, the received signal datasets of commonly used multi-carrier signals, i.e., orthogonal frequency division multiplexing, filter bank multi-carrier, and orthogonal time frequency space, were generated over typical urban multipath channels. The time-frequency images, which were insensitive to modulation parameters, were selected as feature vectors to train the neural network. Secondly, DARTS was employed to automatically search the optimal network architecture. Finally, a joint attention mechanism was introduced into the feature learning process. This mechanism spatially transforming distorted signal features to mitigate the impact of multipath interference, while also calculating and sorting the information weights for each channel of the feature maps to improve the recognition performance of the relevant feature map channels. Simulation results demonstrate that the proposed algorithm improves accuracy in urban multipath channels, especially at low SNR, while simultaneously providing better robustness to modulation parameter variations and small-sample scenarios.
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spelling doaj-art-a21ba8f4067b46e7ae6ba80511b096832025-01-14T07:25:08ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-09-0145142573359474Differentiable architecture search-based automatic modulation recognition for multi-carrier signalsLI JieLI JingLYU LuGONG FengkuiConsidering the lack of a general multi-carrier signal dataset in urban multipath channels, and the challenge of recognizing the modulation types of distorted signals at low signal-to-noise ratio (SNR), a differentiable architecture search-based (DARTS) automatic modulation recognition algorithm for multi-carrier signals was proposed. Firstly, the received signal datasets of commonly used multi-carrier signals, i.e., orthogonal frequency division multiplexing, filter bank multi-carrier, and orthogonal time frequency space, were generated over typical urban multipath channels. The time-frequency images, which were insensitive to modulation parameters, were selected as feature vectors to train the neural network. Secondly, DARTS was employed to automatically search the optimal network architecture. Finally, a joint attention mechanism was introduced into the feature learning process. This mechanism spatially transforming distorted signal features to mitigate the impact of multipath interference, while also calculating and sorting the information weights for each channel of the feature maps to improve the recognition performance of the relevant feature map channels. Simulation results demonstrate that the proposed algorithm improves accuracy in urban multipath channels, especially at low SNR, while simultaneously providing better robustness to modulation parameter variations and small-sample scenarios.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024164/differentiable architecture searchmulti-carrier signalautomatic modulation recognitionurban multipath channeljoint attention mechanism
spellingShingle LI Jie
LI Jing
LYU Lu
GONG Fengkui
Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
Tongxin xuebao
differentiable architecture search
multi-carrier signal
automatic modulation recognition
urban multipath channel
joint attention mechanism
title Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
title_full Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
title_fullStr Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
title_full_unstemmed Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
title_short Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
title_sort differentiable architecture search based automatic modulation recognition for multi carrier signals
topic differentiable architecture search
multi-carrier signal
automatic modulation recognition
urban multipath channel
joint attention mechanism
url http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024164/
work_keys_str_mv AT lijie differentiablearchitecturesearchbasedautomaticmodulationrecognitionformulticarriersignals
AT lijing differentiablearchitecturesearchbasedautomaticmodulationrecognitionformulticarriersignals
AT lyulu differentiablearchitecturesearchbasedautomaticmodulationrecognitionformulticarriersignals
AT gongfengkui differentiablearchitecturesearchbasedautomaticmodulationrecognitionformulticarriersignals