Title
Superimposed training design based on Bayesian optimisation for channel estimation in two-way relay networks
Abstract
In this study, the superimposed training strategy is introduced into orthogonal frequency division multiplexing-modulated amplify-and-forward two-way relay network (TWRN) to perform two-hop transmission-compatible individual channel estimation. Through the superposition of an additional training vector at the relay under power allocation, the separated source-relay channel information can be directly obtained at the destination and then used to estimate the channels. The closed-form Bayesian Crame-r-Rao lower bound (CRLB) is derived for the estimation of block-fading frequency-selective channels with random channel parameters, and orthogonal training vectors from the two source nodes are required to keep the Bayesian CRLB simple because of the self-interference in the TWRN. A set of optimal training vectors designed from the Bayesian CRLB are applied in an iterative linear minimum mean-square-error channel estimation algorithm, and the mean-square-error performance is provided to verify the Bayesian CRLB results.
Year
DOI
Venue
2012
10.1049/iet-com.2012.0418
IET Communications
Keywords
Field
DocType
relay networks (telecommunication),optimisation,bayesian optimisation,bayesian cramer-rao lower bound,training vector,twrn,bayes methods,fading channels,ofdm modulation,orthogonal frequency division multiplexing,block-fading frequency-selective channels,amplify-and-forward two-way relay network,mean-square-error channel estimation,power allocation,superimposed training design,crlb,amplify and forward communication,vectors,mean square error methods
Cramér–Rao bound,Superposition principle,Frequency divider,Upper and lower bounds,Algorithm,Communication channel,Real-time computing,Statistics,Mathematics,Orthogonal frequency-division multiplexing,Relay,Bayesian probability
Journal
Volume
Issue
ISSN
6
18
1751-8628
Citations 
PageRank 
References 
4
0.43
13
Authors
5
Name
Order
Citations
PageRank
Xiaoyan Xu140.43
Jianjun Wu26612.22
Shubo Ren3223.66
Lingyang Song43674238.94
Haige Xiang515430.35