A Symbol Rate Estimation Algorithm Based on Stochastic Resonance Combined with Wavelet Transform

被引：0

作者：

Zhang Z. ^{[1
]}

Ma J.-Q. ^{[1
,2
]}

机构：

[1] PLA Strategic Support Force Information Engineering University, Zhengzhou, 450002, Henan

[2] National Signal Analysis &Processing Experiment Education Demonstration Center, Zhengzhou, 450002, Henan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 12期

关键词：

Parameter-tuning stochastic resonance; Symbol rate; Wavelet transform; Weak signal processing;

D O I：

10.3969/j.issn.0372-2112.2019.12.026

中图分类号：

学科分类号：

摘要：

In non-cooperative communications, due to the deterioration of the channel, the Signal-Noise Ratio (SNR) of the receiving signal is very low in many cases, resulting in the inability to accurately estimate the symbol rate.Stochastic resonance can use noise energy to transfer and amplify the weak signals to some extent, and wavelet transform can effectively detect the instantaneous variation of phase and amplitude of the signals.By using the advantages of both methods, a combination algorithm for estimating the symbol rate of MPSK and MQAM is proposed.First, the adaptive parameter-tuning stochastic resonance is used to match the optimal system parameters for noisy signals, and then the transient information is further extracted by Haar wavelet transform, which not only compensates for the shortcomings of the poor e ffect of using stochastic resonance alone and its easy divergence as a non-linear system, but also reduce the influence of the optimal scale of the wavelet.The simulation result shows that this method can improve the output peak and reduce the SNR threshold, which is suitable for the symbol rate estimation under low SNR. © 2019, Chinese Institute of Electronics. All right reserved.

引用

页码：2647 / 2652

页数：5

共 16 条

[1]

Kohb S., Lee H.S., Detection of symbol rate of unknown digital communication signals, IEE Electronics Letters, 29, 3, pp. 278-279, (1993)

[2]

Chany T., Plews J.W., Ho K.C., Symbol rate estimation by the wavelet transform, Proceedings of IEEE ISCAS' 97, pp. 177-180, (1997)

[3]

Guner A., Kaya I., Periodic variation method for blind symbol rate estimation, International Journal of Communication Systems, 27, 12, pp. 4199-4209, (2015)

[4]

Jin Y., Tian T., Ji H.-B., Symbol rate estimation based on sparse bayesian learning, Journal of Electronics & Information Technology, 40, 7, pp. 1598-1603, (2018)

[5]

Liu M., Zhang J., Li B., Symbol rates estimation of time-frequency overlapped MPSK signals for underlay cognitive radio network, IEEE Access, 6, 99, pp. 16216-16223, (2018)

[6]

Collins J.J., Chow C.C., Capela A.C., Et al., Aperiodic stochastic resonance, Physical Review E, 54, 5, pp. 5575-5584, (1996)

[7]

Xu B., Duan F., Bao R., Et al., Stochastic resonance with tuning system parameters: the application of bistable systems in signal processing, Chaos Solitons & Fractals, 13, 4, pp. 633-644, (2002)

[8]

Kihara R., Yamanashi Y., Yoshikawa N., Power reduction of josephson random access memory using stochastic resonance, IEEE Transactions on Applied Superconductivity, 26, 8, pp. 1-4, (2016)

[9]

He L.-F., Cao L., Zhang G., Weak signal recovery based on power function stochastic resonance, Acta Electronica Sinica, 46, 8, pp. 1906-1914, (2018)

[10]

Liu J., Et al., A Novel parameter-tuned stochastic resonator for binary PAM signal processing at Low SNR, IEEE Communications Letters, 18, 3, pp. 427-430, (2014)

← 1 2 →