Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (11): 3033-3038.

### Improved adaptive linear minimum mean square error channel estimation algorithm in discrete wavelet transform domain based on empirical mode decomposition-singular value decomposition difference spectrum

1. Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China
• Received:2016-05-12 Revised:2016-06-22 Online:2016-11-10 Published:2016-11-12
• Supported by:
This work is partially supported by the National Natural Science Foundation of China(61363076), the Natural Science Foundation of Jiangxi Province(20142BAB207020).

### 基于EMD-SVD差分谱的DWT域LMMSE自适应信道估计算法

1. 江西理工大学 信息工程学院, 江西 赣州 341000
• 通讯作者: 谢斌
• 作者简介:谢斌(1977-),男,江西于都人,副教授,博士研究生,主要研究方向:信号处理、信息安全;杨丽清(1994-),女,江西兴国人,硕士研究生,主要研究方向:信号处理;陈琴(1990-),女,河南信阳人,硕士研究生,主要研究方向:信号处理。
• 基金资助:
国家自然科学基金资助项目（61363076）；江西省自然科学基金资助项目（20142BAB207020）。

Abstract: In view of the problem that the channel estimation error of the current Singular Value Decomposition-Linear Minimum Mean Square Error (SVD-LMMSE) algorithm was relatively large, an improved adaptive Linear Minimum Mean Square Error (LMMSE) channel estimation algorithm in Discrete Wavelet Transform (DWT) domain based on Empirical Mode Decomposition-Singular Value Decomposition (EMD-SVD) difference spectrum was proposed. The DWT was used to quantify the threshold of the signal high frequency coefficients after Least Square (LS) channel estimation and pre-filtering. Then, combined with the adaptive algorithm based on EMD-SVD difference spectrum, the weak signal was extracted from the strong noise wavelet coefficients, and the signal was reconstructed. Finally, the corresponding threshold was set based on Cyclic Prefix (CP) inside and outside the noise's variance of the mean, and the noise of the cyclic prefix length was handled to reduce the further influence of noise. The Bit Error Rate (BER) and the Mean Squared Error (MSE) performances of the algorithm was simulated. The simulation results show that the improved algorithm is better than the classcial LS algorithm, the traditonal LMMSE algorithm and the more popular SVD-LMMSE algorithm and can not only reduce the influence of noise, but also improve the accuracy of channel estimation effectively.

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