Sample covariance matrix eigenvalues based blind SNR estimation

In this paper, a newly developed SNR estimation algorithm is presented. The new algorithm is based on the eigenvalues of the sample covariance matrix of the recieved signal. The presented algorithm is blind in the sense that both the noise and the signal power are unknown and estimated from the received samples. The Minimum Descriptive Length (MDL) criterion is used to split the signal and noise corresponding eigenvalues. The experimental results are judged using the Normalized Mean Square Error (NMSE) between the estimated and the actual SNRs. The results show that, depending on the value of the received vectors size and the number of received vectors, the NMSE is changed and down to −55 dB NMSE can be achieved for the highest used values of the system dimensionality.