An Effective Method of SNR Estimation for LDPC-CPM

The technique of SNR estimation is one of the key technologies in adaptive frequency hopping system. The methods of channel quality estimation for non-linear continuous phase modulation (CPM) signals have some limitations. Therefore, the algorithm of channel quality estimation for CPM signals is worthy of further study. Some similar phase characteristics between sampling CPM and MPSK motivate us to propose a channel estimation algorithm with applications to nonlinear CPM using linear modulation signal processing. A comprehensive analysis of LDPC-CPM schemes using proposed algorithm is presented, and simulation results indicate that the proposed method can not only estimate channel quality well but also make the normalized MSE (NMSE) of SNR estimate close to/less than 0.1 dB at SNR of 4 dB using short block codes. It shows that the algorithm in this paper is effective enough to estimate the signal to noise ratio (SNR). Meanwhile, the algorithm in this paper reduces the complexity of computation compared with other traditional algorithms.

[1]  R. Matzner,et al.  An SNR estimation algorithm using fourth-order moments , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[2]  Jens Zander,et al.  Adaptive frequency hopping in HF communications , 1995 .

[3]  I. Trachanas,et al.  A novel phase based SNR estimation method for constant modulus constellations , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[4]  Norman C. Beaulieu,et al.  A comparison of SNR estimation techniques for the AWGN channel , 2000, IEEE Trans. Commun..

[5]  Zeng Lieguang,et al.  Compressed SNR-and-channel estimation for beam tracking in 60-GHz WLAN , 2015, China Communications.

[6]  Hua Xu,et al.  The simple SNR estimation algorithms for MPSK signals , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[7]  Daoxing Guo,et al.  The performance analysis of LDPC coded SFH/BPSK anti-jamming system , 2015, 2015 International Conference on Wireless Communications & Signal Processing (WCSP).

[8]  Hua Xu,et al.  The Maximum-Likelihood SNR Estimation Algorithm for QAM Signals , 2006, 2006 8th international Conference on Signal Processing.