On the Blind SNR Estimation for IF Signals

Signal-to-noise ratio (SNR) is a widely used significant parameter in communication systems. An eigenvector decomposition and subspace approach is employed for the blind SNR estimation for intermediate frequency (IF) signals in additive white Gaussian noise (AWGN) channel. The signal subspace dimension is determined by the eigenvector decomposition of the sample correlation matrix of the received signals and the minimum description length (MDL) criteria. Then the estimated SNR can be obtained by the computation of the power of the desired signal and the noise, respectively. The proposed algorithm requires no information on the modulation scheme and the parameters of the received signals. Computer simulations are performed for commonly used IF signals, such as MPSK, MFSK and MQAM. The results show that when the true SNR varies from -5dB to 20dB the estimation bias is within 0.6dB and the corresponding standard deviation (STD) is under 0.55