Open AccessApproximate Gibbs algorithm for blind data detection in two‐way relay networks
Author(s) -
Jiang Zhe,
Shen Xiaohong,
Ge Yao,
Wang Haiyan
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.0597
Subject(s) - computer science , relay , gibbs sampling , algorithm , convergence (economics) , constraint (computer aided design) , taylor series , bayesian probability , noise (video) , series (stratigraphy) , exploit , mathematical optimization , artificial intelligence , mathematics , economics , image (mathematics) , biology , economic growth , computer security , mathematical analysis , power (physics) , paleontology , physics , geometry , quantum mechanics
This study investigates the blind data detection in two‐way relay networks (TWRN) that employ amplify‐and‐forward (AF) relay strategy. To blindly detect the data in TWRN in the presence of uncertain time‐frequency offsets and phase noise, the authors develop a new Bayesian‐based approximate Gibbs algorithm based on truncated Taylor series expansion approximation. In addition, the authors exploit available constraint information on parameters of interest. The authors present three receivers based on three different parameter estimation approaches. The authors further discuss the implementation issue and present diagnostic convergence analysis. The authors’ numerical results demonstrate the performance and efficacy of their proposed algorithm.