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AuthorGuo J.
AuthorSong B.
AuthorChi Y.
AuthorJayasinghe L.
AuthorYuen C.
AuthorGuan Y.L.
AuthorDu X.
AuthorGuizani M.
Available date2020-03-18T10:47:16Z
Publication Date2019
Publication NameFuture Generation Computer Systems
ResourceScopus
ISSN0167739X
URIhttp://dx.doi.org/10.1016/j.future.2019.01.041
URIhttp://hdl.handle.net/10576/13425
AbstractUltra-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.
SponsorThis work has been supported by the National Natural Science Foundation of China (No. 61772387 , 61802296 , 61750110529 ), China Postdoctoral Science Foundation Grant (No. 2017M620438 ), the Fundamental Research Funds for the Central Universities ( JB180101 ), Fundamental Research Funds of Ministry of Education and China Mobile ( MCM20170202 ), and also supported by the ISN State Key Laboratory .
Languageen
PublisherElsevier B.V.
SubjectDeep neural network
Loopy factor graph
Message passing
Signal recovery
URLLC
TitleDeep neural network-aided Gaussian message passing detection for ultra-reliable low-latency communications
TypeArticle
Pagination629-638
Volume Number95


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