Performance bounds on fully-data-aided estimation of time-selective channels over relay networks

Abstract

In this paper, the Bayesian Cramer-Rao lower bounds (BCRBs) on dynamic individual channel estimation is examined in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading channel scenario, where the superimposed training framework is adopted. The target of our work is to formulate the nonlinear dynamic state-space equation for individual channels and derive the online/offline BCRBs for full-data-aided (FDA) in-channel estimator, which has a perfect knowledge about the symbols from the source and the superimposed training at the relay. Under the FDA scenario, we calculate closed-form online/offline BCRBs and analyze the effect of the nodes mobility speeds on the BCRB performance. Finally, numerical results are provided to corroborate the proposed studies.