NDA SNR estimation using fourth‐order cross‐moments in time‐varying single‐input multiple‐output channels
Mohamed Bassem Ben Salah, Abdelaziz Samet · IET Communications · 2016
In this study, the authors propose a moment‐based estimator of the signal‐to‐noise ratio (SNR) over time‐variant Rayleigh fading single‐input multiple‐output channels. The correlated time‐variant channel is modelled with the well‐known Jakes’ model. The authors’ approach uses the fourth‐order cross‐moments of the received signal to estimate the SNR with the presence of an additive white Gaussian noise which is uncorrelated between antenna elements. The SNR is deduced by estimating, respectively, the powers of the useful signals and the noise. The proposed SNR estimator is a non‐data‐aided (NDA) method since it does not require a training sequence. The performances of this algorithm are investigated in terms of normalised mean square error over a wide range of scenarios. Simulation results show that the proposed algorithm outperforms the NDA maximum‐likelihood‐based estimators and the moment‐based estimators.