Blind signal-to-noise ratio estimation algorithm with small samples for wireless digital communications

To extend the range of blind signal-to-noise ratio (SNR) estimation and reduce complexity at the same time, a new algorithm is presented based on a signal subspace approach using the sample covariance matrix of the received signal and combined information criterion (CIC) in information theory. CIC overcomes the disadvantages of both Akaike information criterion's (AIC) under penalization and minimum description length's (MDL) over penalization and its likelihood form is deduced. The algorithm needs no prior knowledge of modulation types, baud rate or carrier frequency of the signals. Computer simulation shows that the algorithm can blindly estimate the SNR of digital modulation signals commonly used in additional white Gaussian noise (AWGN) channels and Rayleigh fading channels with small samples, and the mean estimation error is less than 1dB for SNR ranging from -15dB to 25dB. The accuracy and simplicity of this method make it more adapt to engineering applications.