System identification in wireless relay networks via Gaussian process Iterated Conditioning on the Modes estimation

We present a flexible class of stochastic models that are developed for cooperative wireless relay networks systems, in which the relay processing functionality is not known at the destination. The challenge is then to perform system identification in this wireless relay network. We first construct a statistical model based on a representation of the system using Gaussian Processes. We then develop a computationally efficient algorithm which is based on the Iterated Conditioning on the Modes estimation to undertake system identification for each relay in the presence of partial Channel State Information (CSI). We evaluate the identification performance for different non-linear relay functionalities.