Classic Signal-to-Noise Ratio Estimation Algorithm – Maximum Likelihood Estimation

This algorithm represents the classic Maximum Likelihood Estimation method for SNR estimation, which leverages the prior probability density function of the received channel to achieve accurate signal-to-noise ratio measurements. The ML approach demonstrates robust performance in estimating SNR through statistical optimization techniques, typically implemented via numerical methods like gradient ascent or expectation-maximization algorithms.