Data-aided SNR estimation in time-variant Rayleigh fading channels

This paper addresses the data-aided signal-to-noise ratio (SNR) estimation in time-variant flat Rayleigh fading channels. The time-variant fading channel is modeled by considering the Jakes´ model and the first order autoregressive (AR1) model. Closed-form expressions of the Cramér-Rao bound (CRB) for data-aided SNR estimation are derived for fast and slow fading Rayleigh channels. As a special case, the CRB under uncorrelated fading Rayleigh channel is derived. Analytical approximate expressions of the CRB are derived for low and high SNR that enable the derivation of a number of properties that describe the bound´s dependence on key parameters such as SNR, channel correlation, sample number. Since the exact maximum likelihood (ML) estimator is computationally intensive in the case of fast-fading channel, two approximate solutions are proposed for high and low SNR cases. Numerical results illustrate the performance of the estimators and confirm the validity of the theoretical analysis.