How to Use A Priori Information of Data Symbols for SNR Estimation

Author

Dangl, Markus A. ; Lindner, Jürgen

Author_Institution

Dept. of Inf. Technol., Ulm Univ.

Volume

13

Issue

11

fYear

2006

Firstpage

661

Lastpage

664

Abstract

We address the problem of how to use a priori information of data symbols to improve signal-to-noise ratio (SNR) estimation. Digital transmission with binary phase-shift keying (BPSK) over additive white Gaussian noise (AWGN) channels serves as a background. At the receive side, both pilot and data symbols are used to estimate the SNR. In addition, we assume that a priori information of the data symbols is available. Our proposed estimator is then derived as an approximate solution of the maximum-likelihood (ML) approach. A significant improvement in the low-SNR regime over the corresponding estimator without a priori information is shown. Hence, an estimator that uses a priori information of data symbols is suitable to be embedded in iterative decoding schemes like, e.g., turbo decoding or turbo equalization. In addition, the Cramer-Rao lower bound (CRLB) for SNR estimators using a priori information of data symbols is derived

Keywords

AWGN channels; approximation theory; channel coding; iterative decoding; maximum likelihood estimation; phase shift keying; AWGN channel; BPSK; CRLB; Cramer-Rao lower bound; additive white Gaussian noise; apriori information; binary phase-shift keying; data symbol; iterative decoding scheme; maximum-likelihood approach; AWGN; Additive white noise; Binary phase shift keying; Feedback; Iterative decoding; Maximum likelihood decoding; Maximum likelihood estimation; Phase shift keying; Random variables; Signal to noise ratio; A priori information; estimation; iterative decoding; maximum likelihood; signal-to-noise ratio (SNR);

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2005.879478

Filename

1707729