A Code-Aided and Moment-Based Joint SNR Estimation for M-APSK over AWGN Channels

In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a...

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Bibliographic Details
Published in2017 IEEE 85th Vehicular Technology Conference (VTC Spring) pp. 1 - 6
Main Authors Rui He, Dewei Yang, Hua Wang, Jingming Kuang, Xiaojie Wen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
Decoding
Iterative decoding
Maximum likelihood estimation
Measurement
Signal to noise ratio
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Summary:In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a reference measurement of estimation performance. Moreover, a methodology to measure the performance of the estimation by the syndrome of the LDPC codes is derived and the simulation results reveal that the number of 0s in the syndrome presents a positive correlation with the SNR. Compared with code-aided maximum-likelihood (ML) estimators and moment-based estimators for M-APSK signals, it is validated that the proposed joint SNR estimator has integrated the advantages of the classical approaches and simulation results also show that the proposed estimator exploiting the decision metric of the selector performs better at the SNR estimation range.
DOI:10.1109/VTCSpring.2017.8108433