Deep neural network-aided Gaussian message passing detection for ultra-reliable low-latency communications

Jie Guo, Bin Song, Yuhao Chi, Lahiru Jayasinghe, Chau Yuen, Yong Liang Guan, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)629-638
Number of pages10
JournalFuture Generation Computer Systems
Volume95
DOIs
StatePublished - Jun 2019

Keywords

  • Deep neural network
  • Loopy factor graph
  • Message passing
  • Signal recovery
  • URLLC

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