通信学报2024，Vol.45Issue(9)：14-25,12.DOI:10.11959/j.issn.1000-436x.2024164
基于可微分架构搜索的多载波信号自动调制识别
Differentiable architecture search-based automatic modulation recognition for multi-carrier signals
摘要
Abstract
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 re-ceived 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 atten-tion 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.Simula-tion 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.关键词
可微分架构搜索/多载波信号/自动调制识别/城市多径信道/联合注意力机制Key words
differentiable architecture search/multi-carrier signal/automatic modulation recognition/urban multipath channel/joint attention mechanism分类
电子信息工程引用本文复制引用
李杰,李靖,吕璐,宫丰奎..基于可微分架构搜索的多载波信号自动调制识别[J].通信学报,2024,45(9):14-25,12.基金项目
国家自然科学基金资助项目(No.62271368,No.62001354) （No.62271368,No.62001354）
陕西省重点研发计划基金资助项目(No.2023-YBGY-041) （No.2023-YBGY-041）
中国博士后科学基金资助项目(No.BX20190264,No.2019M650258)The National Natural Science Foundation of China(No.62271368,No.62001354),The Key Research and De-velopment Program of Shaanxi Province(No.2023-YBGY-041),China Postdoctoral Science Foundation Project(No.BX20190264,No.2019M650258) （No.BX20190264,No.2019M650258）
