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 Cramer-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.