Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems  

作　　者：Mohamed Hassan Essai Ali Fahad Alraddady Mo’ath Y.Al-Thunaibat Shaima Elnazer 

机构地区：[1]Department of Electrical Engineering,Faculty of Engineering,Al-Azhar University,Qena,83513,Egypt [2]Department of Computer Engineering,College of Computers and Information Technology,Taif University,Taif,21944,Saudi Arabia [3]Department of Management Information Systems,Taif University,Taif,21944,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2023年第4期755-778,共24页工程与科学中的计算机建模（英文）

基　　金：funded by Taif University Researchers Supporting Project No.(TURSP-2020/214),Taif University,Taif,Saudi Arabia。

摘　　要：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 proposedCNN-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.

关 键 词：DLNNs channel state estimator 5G and beyond communication systems robust loss functions 

分 类 号：TN929.5[电子电信—通信与信息系统]