A BLIND SIGNAL-TO-NOISE RATIO ESTIMATOR FOR DIGITAL BANDPASS SIGNALS BASED ON A MODIFIED ITERATIVE SUBSPACE TRACKING ALGORITHM
Dan Sui, Lindong Ge · 2007
Signal-to-Noise Ratio (SNR) is an important parameter in communication systems. The SNR for the digital bandpass signals can be estimated by the Eigenvector Decomposition (ED) of the correlation matrix of the received data. But the heavy compu- tational load confines its application. In this paper a new blind SNR estimator based on an iterative subspace tracking algorithm, called the modified Projection Approximation Subspace Tracking (PASTd), is proposed for the bandpass signals in the complex Addi- tive White Gaussian Noise (AWGN). Further in order to guarantee the orthogonality of the estimated eigenvectors, a modified Gram-Schmidt method is introduced into the original PASTd algorithm. Compared with the ED-based method, the proposed algorithm can achieve a more accurate estimation with a simple computational complexity, thus is effective especially for on-line use. Simulations performed for the commonly used digital bandpass signals, such as 2/4/8 Phase-Shift Keying (PSK) and 16/64/128/256 Quadra- ture Amplitude Modulated (QAM) signals, verify its feasibility.