TY - JOUR
T1 - Deep neural network-aided Gaussian message passing detection for ultra-reliable low-latency communications
AU - Guo, Jie
AU - Song, Bin
AU - Chi, Yuhao
AU - Jayasinghe, Lahiru
AU - Yuen, Chau
AU - Guan, Yong Liang
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/6
Y1 - 2019/6
N2 - Ultra-reliable low-latency communications (URLLC) is a key technology in 5G supporting real-time multimedia services, which requires a low-cost signal recovery technology in the physical layer. A kind of well-known low-complexity signal detection is message passing algorithm (MPA) based on factor graph. However, reliability and robustness of MPA are deteriorated when there are cycles in factor graph. To address this issue, we propose two novel Gaussian message passing (GMP) algorithms with the aid of deep neural network (DNN), in which the network architectures consist of two DNNs associated with detections for mean and variance of the signal. Particularly, the network architecture is constructed by transforming the factor graph and message update functions of the original GMP algorithm from node-type into edge-type. Then, weights and bias parameters are assigned in the network architecture. With the aid of deep learning methods, the optimal weights and bias parameters are obtained. Numerical results demonstrate that two proposed DNN-aided GMP algorithms can significantly improve the convergence of original GMP algorithm and also achieve robust performances in the cases without prior information.
AB - Ultra-reliable low-latency communications (URLLC) is a key technology in 5G supporting real-time multimedia services, which requires a low-cost signal recovery technology in the physical layer. A kind of well-known low-complexity signal detection is message passing algorithm (MPA) based on factor graph. However, reliability and robustness of MPA are deteriorated when there are cycles in factor graph. To address this issue, we propose two novel Gaussian message passing (GMP) algorithms with the aid of deep neural network (DNN), in which the network architectures consist of two DNNs associated with detections for mean and variance of the signal. Particularly, the network architecture is constructed by transforming the factor graph and message update functions of the original GMP algorithm from node-type into edge-type. Then, weights and bias parameters are assigned in the network architecture. With the aid of deep learning methods, the optimal weights and bias parameters are obtained. Numerical results demonstrate that two proposed DNN-aided GMP algorithms can significantly improve the convergence of original GMP algorithm and also achieve robust performances in the cases without prior information.
KW - Deep neural network
KW - Loopy factor graph
KW - Message passing
KW - Signal recovery
KW - URLLC
UR - http://www.scopus.com/inward/record.url?scp=85060922018&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060922018&partnerID=8YFLogxK
U2 - 10.1016/j.future.2019.01.041
DO - 10.1016/j.future.2019.01.041
M3 - Article
AN - SCOPUS:85060922018
SN - 0167-739X
VL - 95
SP - 629
EP - 638
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -