Convolutional neural network based blind automatic modulation classification robust to phase and frequency offsets

Jin, Heetae; Jeong, Sinjin; Park, Dong Chan; Kim, Suk Chan

doi:10.1049/iet-com.2019.0980

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Automatic modulation classification (AMC) refers to classifying modulation types of received signals under non-cooperative communication scenarios [1]. AMC is one of the core technologies for many applications such as spectrum management, cognitive radio, adaptive modulation, and hostile communications [2][3][4][5]. Exiting work on AMC is categorized into the likelihood-based (LB), feature-based (FB), and deep neural network-based (DB) approaches.…”

Section: Introductionmentioning

confidence: 99%

Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

Hao

Dang

et al. 2023

IET Communications

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Deep neural networks (DNN) based automatic modulation classification (AMC) has achieved high accuracy performance. However, DNNs are data‐hungry models, and training such a model requires a large volume of data. Insufficient training data will cause DNN models to experience overfitting and severe performance degradation. In practical AMC tasks, training the deep model with sufficient data is challenging due to the costly data collection. To this end, a novel probability density function (PDF) based data augmentation scheme and a method to determine the required minimum sampling size for data enlargement is proposed. Compared with the known image‐based augmentation scheme, the proposed waveform‐based PDF technique has low complexity and is easy to implement. Experimental results show that the required size of the training dataset is one order of magnitude smaller than the sufficient dataset in the additive white Gaussian noise channel, and effective recognition can be achieved using around 60% of the total examples under the Rayleigh channel. Moreover, the presented scheme can expand training data under frequency and phase offsets.

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