… , a multi-task learning model that can perform multiple signal characterization tasks with a …
However, due to the novel nature of multi-task learning as applied to signal characterization, …

… learning (DL)-based AMC is believed as one of the most promising methods with great
classification … In this paper, a novel multi-task learning (MTL)based generalized AMC method is …

… head of SNR-SCH is used to predict whether the signal is high SNR or low SNR (ie, the
SNR prediction task is different from the modulation classification.), we think the DNN model …

… achieves a modulation classification accuracy … -task learning framework to solve two
challenging and fundamental wireless signal recognition tasks - modulation and signal classification…

… an antenna array, analogue to digital converters (ADCs), a digital signal processor (DSP),
a multi-task learning model of modulation classification and DOA estimation with a display. …

… Yang, and AE Gamal, “Deep neural network architectures for modulation classification,” in
2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, oct 2017. [16] K. …

… examples: 1) Diverse modulation schemes: In distinct situations, some modulation modes
are added or removed, depending on the requirement of the classification task. Except for the …

… only focused on a single task – modulation or signal (protocol) classification – which in … -task
learning (MTL) framework to simultaneously learn modulation and signal classification tasks …

… modulation classification task, and suggesting methods for reducing their training time. First,
we study different deep neural network architectures for the task of modulation classification, …

… of modulation classification and, specifically, focuses on the signal representation and data
preprocessing aspect in modulation classification. … The task of modulation classification is to …