… 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. …

… (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 …

… -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 …

… 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 …

… model based on deep learning, which consists of a backbone network and a mask-based …
signal classification models to compare with our AMSCN model. Some of these models have …

… Obviously our modulation classification is a multiple composite hypothesis testing problem,
due to the unknown data symbols {sf)], as well as the unknown parameters, which are a, and …

… We measure both box and mask mAP, and report the results for both small and large
receptive field models. Comparing with Swin Transformer, FocalNets improve the box mAP (APb) …