@inproceedings{81f702f769264ff1a74896ccce246d32,
title = "A Code-Aided and Moment-Based Joint SNR Estimation for M-APSK over AWGN Channels",
abstract = "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 momentbased 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.",
keywords = "APSK, LDPC code, SNR estimation, Syndrome",
author = "Rui He and Dewei Yang and Hua Wang and Jingming Kuang and Xiaojie Wen",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 85th IEEE Vehicular Technology Conference, VTC 2017-Spring ; Conference date: 04-06-2017 Through 07-06-2017",
year = "2017",
month = nov,
day = "14",
doi = "10.1109/VTCSpring.2017.8108433",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings",
address = "United States",
}