On a New SNR Estimation Approach With Polar Codes
Chaofan Chen, Jiayi Wu, Changhong Wang, Xiaqing Miao, Xiangyuan Bu · 2021
A maximum likelihood polar code-aided (ML-PCA) SNR estimation algorithm is proposed in this paper. In the ML-PCA algorithm, the posterior soft information of polar BP decoding is adopted to help the SNR estimation process. Benefiting from the excellent performance of polar code, the ML-PCA algorithm is able to acquire more reliable decoding information and further improve the SNR estimation quality. By detailed derivation, the SNR estimator and Cramér-Rao Lower Bound (CRLB) of the proposed algorithm are achieved. Numerical simulations demonstrate that the ML-PCA algorithm considerably outperforms the conventional M2M4 and ML-NDA algorithms.