… turn improves the modulation classification performance under … directions in the area of DL
for modulation classification. … network architectures which cannot share any connections and …

… After extracting features from multimodal information using Resnet, we adopt a feature
fusion strategy to merge multimodal features of signals to obtain more discriminating features. …

An automatic modulation classification has a very broad application in wireless … convolutional
neural network (CNN)-based modulation classification. However, the duration of the actual …

… to reconstruct temporal and spatial features separately, which … feature sharing reconstructing
network (STARNet) to simultaneously extract low-dimensional spatial and temporal feature …

… (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to …
for modulation classification, where the output sub-images from the slottedCD feature extractor …

… the query set share the same feature representation (ie the … feature fusion, however, satisfying
classification performance can be guaranteed only when the spatial and temporal features …

… below to share a full-… modulation classification algorithm to discriminate the radar emitter
signals exploiting deep learning of convolutional neural network (CNN) based on a joint feature …

… Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by
sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited …

… extracted features. The proposed design is demonstrated to provide 5% improvement in
modulation classification compared to alternative designs lacking either feature sharing or …

… the modulation type, either in-band or out-ofband. Automatic modulation classification can
… in a shared band. Utilizing AMC in the cognitive radio, the receiver can detect the change in …