Title :
A Blind SNR Estimator Based on Iterative Subspace Tracking Algorithm for Digital Modulated Signals
Author :
Sui, Dan ; Ge, Lindong ; Wang, Qing ; Zhang, Hui
Author_Institution :
Zhengzhou Inf. Sci. & Technol. Inst.
Abstract :
Signal-to-noise ratio (SNR) is an important parameter in turbo soft decoding. In this paper a blind SNR estimator for digital modulated signals in the complex additive white Gaussian noise (AWGN) channel is proposed. The algorithm uses the eigenvalues of the covariance of the received signal. And the eigenvalues are estimated via an iterative subspace tracking algorithm, known as the projection approximation subspace tracking (PASTd) algorithm. The orthonormality of the estimated eigenvectors is guaranteed by the use of the Gram-Schmidt method. Computer simulations are performed for 2/4/8 PSK signals when the true SNR is in the range from 3 dB to 25 dB. Compared with the eigenvalue decomposition (ED)-based method, the proposed algorithm can achieve a comparable estimation but with a significantly reduced computational complexity
Keywords :
AWGN channels; computational complexity; decoding; eigenvalues and eigenfunctions; iterative methods; phase shift keying; turbo codes; Gram-Schmidt method; PSK signals; blind SNR estimator; complex additive white Gaussian noise channel; computational complexity; covariance eigenvalues; digital modulated signals; eigenvalue decomposition; eigenvectors estimation; iterative subspace tracking algorithm; projection approximation subspace tracking algorithm; signal-to-noise ratio; turbo soft decoding; AWGN; Additive white noise; Approximation algorithms; Computer simulation; Digital modulation; Eigenvalues and eigenfunctions; Iterative algorithms; Iterative decoding; Phase shift keying; Signal to noise ratio;
Conference_Titel :
Information Theory Workshop, 2006. ITW '06 Chengdu. IEEE
Conference_Location :
Chengdu
Print_ISBN :
1-4244-0067-8
Electronic_ISBN :
1-4244-0068-6
DOI :
10.1109/ITW2.2006.323708
