一种基于小波去噪的DFT信道估计改进算法

计算机工程与科学

一种基于小波去噪的DFT信道估计改进算法

谢斌，乐鸿浩，陈博

（江西理工大学信息工程学院，江西赣州 341000）

收稿日期:2015-04-14 修回日期:2015-09-22 出版日期:2016-09-25 发布日期:2016-09-25
基金资助:
国家自然科学基金（61363076）；江西省自然科学基金（20142BAB207020）

An improved DFT channel estimation algorithm based on wavelet denoising

XIE Bin，LE Hong-hao，CHEN Bo

（School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China）

Received:2015-04-14 Revised:2015-09-22 Online:2016-09-25 Published:2016-09-25

摘要/Abstract

摘要：

针对小波去噪与DFT插值相结合的信道估计算法没有对循环前缀内的噪声进行去噪的缺点，提出了一种基于小波去噪与改进的DFT插值相结合的信道估计新算法。该算法首先利用离散小波变换对最小二乘（LS）法估计出的结果进行阈值去噪处理，并根据循环前缀内、外噪声方差的均值在DFT插值的过程中设置相应门限，然后对循环前缀内的噪声再次处理，以进一步减小噪声的影响。仿真实验结果表明，在复杂度基本不变的前提下，该算法能够较好地减小加性高斯白噪声的影响，并有效提升信道估计的准确度，其总体性能较小波去噪与DFT插值相结合的信道估计算法更优。

关键词: 正交频分复用, 信道估计, 小波去噪, 离散傅里叶变换

Abstract:

Given that the channel estimation algorithm which combines wavelet de-noising with interpolation in DFT cannot eliminate the noise in the cyclic prefix length, we propose a new channel estimation algorithm, a combination of wavelet de-noising and the improved DFT interpolation. This channel estimation algorithm first uses the discrete wavelet transform of least squares (LS) method to estimate the results of threshold de-noising processing, and sets the corresponding threshold in the process of DFT interpolation based on the noise variance of the mean inside and outside cyclic prefix. Then the noise of the cyclic prefix length is handled again to reduce further noise influence. Simulation results show that under the premise of unchanged complexity, the proposed algorithm cannot only remove the influence of additive white Gauss noise better, but also improve the accuracy of channel estimation effectively. In general, the channel estimation algorithm performance is better than the combination of wavelet de-noising and DFT interpolation.

Key words: OFDM, channel estimation, wavelet de-noising, DFT

谢斌，乐鸿浩，陈博. 一种基于小波去噪的DFT信道估计改进算法[J]. 计算机工程与科学.

XIE Bin，LE Hong-hao，CHEN Bo. An improved DFT channel estimation algorithm based on wavelet denoising [J]. Computer Engineering & Science.

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