Abstract :
A subspace-based blind signal-to-noise ratio (SNR) estimation algorithm for digital bandpass signals in additive white Gaussian noise (AWGN) channel is developed in this paper. The eigenvector decomposition of the sample correlation matrix of the received signals is performed first, then the signal subspace dimension is determined, the power of the signal and the noise is evaluated respectively, finally the estimated SNR is obtained. The performance analysis is derived and computer simulation is also done, specifically for the commonly used signals such as MPSK (M=2, 4, 8), MFSK (M=2, 4) and MQAM (M=16, 64, 128, 256). The results show that the estimated bias is within 1 dB and the corresponding standard deviation (STD) is below 0.55 when the true SNR varies from -5 dB to 25 dB. Also the proposed algorithm requires no information on the modulation scheme and the parameters of the received signals
Keywords :
AWGN channels; eigenvalues and eigenfunctions; matrix algebra; signal processing; AWGN channel; additive white Gaussian noise; correlation matrix; digital bandpass signals; eigenvector decomposition; signal subspace dimension; AWGN; Additive white noise; Baseband; Gaussian noise; Multiaccess communication; Performance analysis; Signal analysis; Signal to noise ratio; Statistics; Tin;
