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.

Key words: Orthogonal Frequency Division Multiplexing (OFDM), Empirical Mode Decomposition (EMD), Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT), channel estimation

摘要： 针对当前基于奇异值分解的线性最小均方误差（SVD-LMMSE）法信道估计误差相对较大的问题，提出了一种基于经验模态分解和奇异值分解（EMD-SVD）差分谱的离散小波变换（DWT）域线性最小均方误差（LMMSE）自适应信道估计算法。在对信号进行最小二乘（LS）信道估计及预滤波处理后，运用DWT对信号的高频系数进行阈值量化去噪处理；然后结合基于EMD-SVD差分谱的自适应算法，将强噪声小波系数中微弱的有效信号提取出来，并进行信号的重构；最后根据循环前缀（CP）内、外噪声方差的均值设置相应门限，对循环前缀以内的噪声进行再次处理，从而进一步降低噪声的影响。对算法的误码率（BER）和均方误差（MSE）性能进行实验仿真，实验结果表明：所提算法的整体性能明显优于经典的LS算法、传统的LMMSE算法和目前较为流行的SVD-LMMSE算法，能够较好地降低噪声的影响，并可有效提升信道估计的精确度。

关键词: 正交频分复用, 经验模态分解, 奇异值分解, 离散小波变换, 信道估计