Cramer-Rao Lower Bound for NDA SNR Estimation from Linear Modulation Schemes over Flat Rayleigh Fading Channel

In this contribution, Cramer-Rao lower bound (CRLB) for signalto-noise ratio (SNR) estimation from linear modulation signals over flat Rayleigh fading channel is addressed. Therefore, we derive the analytical expressions of Fisher information matrix entries that assess the optimal variance of any unbiased SNR estimator. Based on statistical Monte Carlo computing method, simulation results are drawn from several constellation densities and observation window sizes. For the linear modulation schemes used here, it is shown that the lower bound is as higher as the modulation order increases. The derived bound provides an efficient standard for evaluating the performance of any unbiased non-data aided (NDA) SNR estimator from linear modulation signals over flat Rayleigh fading channel (FRFC).