… identification technology to obtain the enemy’s modulation type information [11]. … modulation
classification (AMC) method based on deep residual neural network with masked modeling (…

… They masked and predicted the raw signal sequences, … the classification performance, a
Masked Autoencoder (MAE) is redesigned for modulation classification originally by modeling …

… advantages for automatic modulation classification (AMC) with … within signals, a novel masked
contrastive learning with hard … with temporal masking to enable robust temporal modeling. …

… Finally, we introduce several mask models and analyze the feasibility of masked signal …
Here, to prove there superiority, we apply them directly to modulation classification task and …

… (2) Masking filters are designed to … three masks to generate the color image, and each of
the masks is designed to extract the information of one class. (3) Selected classification models …

… of modulation classification and, specifically, focuses on the signal representation and data
preprocessing aspect in modulation classification. … In [52], masking filters are implemented to …

… -art models of CNN-based AMC (ie, RobustCNN [8] and MCnet [7]). Then, we compare the
modulation classification accuracy between the models … A detailed description of the masking …

… masking modeling refers to a technique where a portion of an image or a sequence of
data is masked… The model is then required to predict the masked part based on the context …

… On two datasets, when the SNR is lower than −5 dB, DH-TR and baselines perform similarly,
which indicates that all models cannot perform modulation classification well under low …

… In Section II, signal models and samples of their features are presented. In Section III, FB …
The study has investigated the classification of MASK, MPSK, and MFSK modulations. Although …