Mohamed Hassan Essai Ali1,*, Fahad Alraddady2, Mo’ath Y. Al-Thunaibat3, Shaima Elnazer2
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 755-778, 2023, DOI:10.32604/cmes.2022.022246
Abstract For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a
robust channel state estimator (CSE). The proposed CSE extracts channel information from transmit-and-receive
pairs through offline training to estimate the channel state information. Also, it utilizes pilots to offer more helpful
information about the communication channel. The proposed CNN-CSE performance is compared with previously
published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs. The CNN-CSE
achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared
with BiLSTM and LSTM-based estimators. Using three different loss function-based classification layers and… More >
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