@Article{cmes.2022.022246,
AUTHOR = {Mohamed Hassan Essai Ali, Fahad Alraddady, Mo’ath Y. Al-Thunaibat, Shaima Elnazer},
TITLE = {Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {135},
YEAR = {2023},
NUMBER = {1},
PAGES = {755--778},
URL = {http://www.techscience.com/CMES/v135n1/50093},
ISSN = {1526-1506},
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 the
Adam optimization algorithm, a comparative study was conducted to assess the performance of the presented
DNNs-based CSEs. The BiLSTM-CSE outperforms LSTM, CNN, conventional least squares (LS), and minimum
mean square error (MMSE) CSEs. In addition, the computational and learning time complexities for DNN-CSEs
are provided. These estimators are promising for 5G and future communication systems because they can analyze
large amounts of data, discover statistical dependencies, learn correlations between features, and generalize the
gotten knowledge.},
DOI = {10.32604/cmes.2022.022246}
}