… [31] as a variant of the classical FMCW modulation is … with the regular convolution and the
masked-l1 loss. Note that … with 50 randomly generated masks are fed into different models for …

… It brings improvements in perplexity for masked sequence modeling tasks. The talking-heads …
Yao, “Modulation classification based on signal constellation diagrams and deep learning,” …

… universal adversarial attacks on AMC models, and thus an … on two radio signal datasets
and models. In most scenarios, … This study emphasized the vulnerability of ACM models to …

… applied in automatic modulation classification by extracting and … learningbased models
classify radio modulation categories. … Then, we feed the masked samples into the LSTMbased …

… [9] designed a deep residual neural network (DRMM) based on masked modeling to improve
the AMC accuracy of deep learning with limited signal samples. Shen et al. [10] developed …

… -inspired modulation processing can substantially improve the mask estimation accuracy in
… modulation filters were scaled logarithmically, inspired by findings from auditory modeling. …

… models of the masks, where every spectral feature is classified as reliable or masked, and is
… This classification strategy results in sparse and isolated masked features, like the squares …

… are other approaches for modeling modulated signals, but the … Wu, and RQ Hu, “A novel
automatic modulation classification … Sun, “Masked label prediction: Unified message passing …

… masked frequency modeling as a pre-training method to reduce the artifacts generated by
overestimation. In addition, we apply a feature-wise linear modulation … for modeling frequency …

… This paper is organized as follows: In section IV we describe the different approaches to
attentional modulation that we test. In section V we describe the experimental results. Methods …