Y Wang, G Gui, T Ohtsuki… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
… learning (DL)-based AMC is believed as one of the most promising methods with great classification … In this paper, a novel multi-tasklearning (MTL)based generalized AMC method is …
S Chang, S Huang, R Zhang, Z Feng… - IEEE internet of things …, 2021 - ieeexplore.ieee.org
… 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 modulationclassification.), we think the DNN model …
… achieves a modulationclassification accuracy … -tasklearning 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-tasklearning model of modulationclassification and DOA estimation with a display. …
… Yang, and AE Gamal, “Deep neural network architectures for modulationclassification,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, oct 2017. [16] K. …
F Meng, P Chen, L Wu, X Wang - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
… examples: 1) Diverse modulation schemes: In distinct situations, some modulation modes are added or removed, depending on the requirement of the classificationtask. Except for the …
… only focused on a single task – modulation or signal (protocol) classification – which in … -task learning (MTL) framework to simultaneously learnmodulation and signal classificationtasks …
… modulationclassificationtask, and suggesting methods for reducing their training time. First, we study different deep neural network architectures for the task of modulationclassification, …
S Peng, S Sun, YD Yao - … on Neural Networks and Learning …, 2021 - ieeexplore.ieee.org
… of modulationclassification and, specifically, focuses on the signal representation and data preprocessing aspect in modulationclassification. … The task of modulationclassification is to …