ZTE Communications ›› 2019, Vol. 17 ›› Issue (4): 3-11.doi: 10.12142/ZTECOM.201904002
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XUE Songyan1, LI Ang1, WANG Jinfei1, YI Na1, MA Yi1(
), Rahim TAFAZOLLI1, Terence DODGSON2
Received:2019-09-19
Online:2019-12-25
Published:2020-04-16
About author:XUE Songyan received the B.E. degree from University of Electronic Science and Technology of China and M.S. degree from University of Surrey, the United Kingdom. He is currently pursuing the Ph.D. degree at the Institute of Communication Systems (ICS), University of Surrey. His research interests center around deep learning applications in wireless communication physical layer.|LI Ang received the B.E. degree from Northwestern Polytechnical University, China, and M.S. degree from University of Surrey. He is currently pursuing the Ph.D. degree at the Institute of Communication Systems (ICS), University of Surrey. His research interests focus on the multi-carrier waveform design for future physical layer, machine learning and non-linear transceiver optimization.|WANG Jin fei received the B.S. degree from University of Science and Technology of China, and M.S. degree from University of Surrey. He is currently pursuing the Ph.D. degree at the Institute of Communication Systems (ICS), University of Surrey. His research interests include mobile edge computing and URLLC.|YI Na is the Founder and Director of DEEPGO Ltd. She is also a Senior Research Fellow in Institute for Communication Systems (ICS), University of Surrey. She has had 15-year experience in coordination of international research projects funded by European Commission or EPSRC. She has established rich cooperation across EU, China and other parts of Asia. Her current research interest focuses on machine learning for wireless networks, large-scale 5G pilot platform, and C2X communications.|MA Yis the Head of AI Wireless Group within the Institute of Communication Systems (ICS), University of Surrey, the United Kingdom. He has authored and co-authored 150+ IEEE journal and conference papers as well as 5 patents in the field of multiuser information theory, signal processing and machine learning for wireless communications.Supported by:XUE Songyan, LI Ang, WANG Jinfei, YI Na, MA Yi, Rahim TAFAZOLLI, Terence DODGSON. To Learn or Not to Learn:Deep Learning Assisted Wireless Modem Design[J]. ZTE Communications, 2019, 17(4): 3-11.
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Figure 1.
A simple example of Level B communication problem (semantic communication)."
Figure 2.
Block diagram of the physical-layer (PHY) procedure of point-to-point communication."
Figure 3.
An artiflcial neural network (AN-assisted parallel decoder: (a) two-stage parallel decoding and (b) AN-assisted sub- codeword decoder.There are three hidden layers, with each employing Rectified Linear Unit (ReLU) activation function; the output layer is equipped with sigmoid activaltion function, which outputs the estimated original information bits."
Figure 4.
Bit reliability and latency evaluation for decoding of halfrate non-recursive convolutional codes with a codeword length of 64."
Figure 5.
Block diagram of the deep artifcial neural network (deep- ANN) assisted lassification- and-then-multiuser detection (CAT-MUD)."
Figure 6.
BLER of classification-and-then -multiuser-detection (CATMUD) as a function of Eb/N0 (dB) over Rayleigh fading channels."
Figure 7.
Block diagram of deep-learning assisted multiuser multipleinput multiple-out (MU-MIMO) detection algorithms."
Figure 8.
Block diagram of the DNN-PIC approach."
Figure 9.
Average BER as a function of Eb/N0 for uncoded 4-by-8 multiuser multiple- input multiple-out (MU-SIMO) system with BPSK modulation."
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