In view of the problem that the traditional channel estimation algorithm based on Discrete Cosine Transform (DCT) does not eliminate the noise in the cyclic prefix length, a new method of Orthogonal Frequency Division Multiplexing (OFDM) system channel estimation based on wavelet de-noising and DCT interpolation was proposed. First the method of Least Squares (LS) was used to preliminarily estimate channel for received pilot signal, then the results estimated by LS method were processed through discrete wavelet thresholding denoising, finally the noise of the cyclic prefix length was handled again by DCT interpolation algorithm to further reduce the influence of noise. The simulation on Matlab 2012 platform, compared with the traditional channel estimation algorithm based on DCT, under the conditions of the same Bit-Error-Rate (BER), the Signal-to-Noise Rate (SNR) performance of the proposed algorithm improved about 1 dB; under the conditions of the same Mean-Square-Error (MSE), the SNR performance of the proposed algorithm improved about 2 dB.The simulation results show that the proposed algorithm can not only reduce the influence of Additive White Gaussian Noise (AWGN), but also improve the accuracy of channel estimation effectively, and the proposed algorithm has better performances than the channel estimation algorithm based on DCT.

Traditional Linear Minimum Mean Square Error (LMMSE) channel estimation was required to know the statistical characteristics of the channel. However, these characteristics are usually unknown in practical applications. Aiming at the uncertainty of wireless channel statistics, taking the time-domin channel sparsity of the energy distribution into consideration, this article proposed an improved LMMSE channel estimation algorithm based on Least Squares (LS) estimation. The algorithm began with the highest confidence degree subcarrier, making the adjacent subcarrier channel estimation value as the current subcarrier real response to compute the weighting coefficient, then to complete channel response of the multiple channels by the method of weighted average. This algorithm avoided the complicated operation of the matrix inversion and decomposition, and might be done effectively and easily. The experimental results show that the performance of the improved algorithm is better than LS and the SVD-LMMSE (Singular Value Decomposition-Linear Minimum Mean Square Error) channel estimation, and the Bit Error Ratio (BER) is close to traditional LMMSE algorithm.