… Recently, thanks to its strong learning ability and low testing complexity, deepneuralnetwork (DNN) has made great success in optimization problems in wirelesscommunication, such …
… -based approach for wireless resource management. The … -linear mapping and to use a deepneuralnetwork (DNN) to … some algorithms of interest in wirelesscommunications. We use …
I Hameed, PV Tuan, I Koo - Applied Sciences, 2020 - mdpi.com
… a wireless powered communicationnetwork (WPCN). We provide a study and analysis of a deepneuralnetwork … In this scheme, the deepneuralnetwork provides an optimized solution …
B Zhu, J Wang, L He, J Song - … Areas in Communications, 2019 - ieeexplore.ieee.org
… a novel neuralnetwork structure for jointly optimizing the transmitter and receiver in communication … The property of convolutionalneuralnetwork enables the network to process input …
… Since the first generation (1G) wirelesscommunicationnetwork entered the market in the 1980s, the world has been dramatically changed by the development of mobile communication …
Y An, S Wang, L Zhao, Z Ji, I Ganchev - IEEE Access, 2023 - ieeexplore.ieee.org
… systems, especially when there are many wirelesscommunication devices and various noise and interference sources present in the environment. The deepneuralnetwork (DNN) …
… [12] modified the widely used belief propagation (BP) algorithm by cascading a Convolutional NeuralNetwork (CNN) to the standard BP decoder, which succeeds in outputting a more …
MHE Ali, ML Rabeh, S Hekal, AN Abbas - IEEE Access, 2022 - ieeexplore.ieee.org
… in this paper, we propose DL gated recurrent unit (GRU) neuralnetworks for channel estimation of data subcarriers as an innovative approach where the computation requirements can …
T Zhang, S Mao - … Transactions on Cognitive Communications …, 2019 - ieeexplore.ieee.org
… In this paper, we exploit the popular convolutionalneuralnetworks (CNNs) to capture the spatial local correlation by enforcing a local connectivity pattern among the neurons of …