Research On Nonparametric Channel Estimation Algorithm For OFDM Systems

Along with the extensive deployment of 3G wireless mobile communication systems, which are based on IMT2000 standard, the innovative B3G or 4G broadband multimedia communication techniques are widely investigated. To adapt to the need for multimedia services, the aim of the B3G/4G systems is achieving a transmission rate of 100Mbps in high-mobility environment and 1Gbps in low-mobility environment. So there must be some major progress in frequency efficiency and transmission rate in these systems.OFDM is a multi-carrier modulation or transmission technique. It has the advantages of high frequency, resistant to multipath delay spread and realization simplisity etc., therefore is chosen as one of the core techniques of B3G/4G communication system. To transmission in OFDM system with high data rates while reserving high frequency efficiency, big constelation mapping and coherent detection must be employed. In this situation, the receiver must know the channel state information. Otherwise, many transmission schemes such as adaptive modulation and coding, space time transmission, MIMO, beamforming and crosslayer processing, all have to know the CSI. So channel estimation is a essential part of OFDM systems.Our focus is on the nonparametric estimation based OFDM channel estimation algorithm. In the dissertation, the radio fading channel property and nonparametric estimation theory were firstly discussed, then some channel estimation algorithm based on nonparametric function estimation were proposed and analysed after the briefly intruducing of conventional LS estimator, LMMSE estimator and DFT base transform domain estimator. These algorithm do not neen the a priori information of the channel and transmitted data, and can highly improve estimation performance only at a cost of computation complexity O(N).In the first part, the properties of the radio fading channel were firstly discusseded, then the channel mode and its mathmatical characters were carefully analysed and the nonparametric estimation theory was introduced. Finally the conclusion was drawn that the radio fading channel can be estimated by nonparametric function estimation. Therefore a solid theorical basis is laid for the rest part of the dissertation. In the second part, a nonparametric frequecy domain channel estimation algorithm based on local linear regression was proposed. In this method, the interpolation processing and noise cancellatiod processing were seperated, the LS estimated channel gain at pilot tones were firstly interpolated by piece-wise constant or piece-wise linear interpolation and then smoothed by local linear regression. This method can efficiently suppress the channel and interpolation error as well as avoid the windowing effect of the DFT based algorithm, so the performance was improved greatly.To enhence the accuracy of the estimation while reserving the computational complexity, a nonparametric frequecy domain channel estimation algorithm based on Savitzky-Golay smoothing filter was proposed in the third part. Savitzky-Golay smoothing filter can be considered as a local polynomial estimator, and can be calculated by convolution. Because the filter length is always far more longer then the order of the regression polynomial, the computional complexty can be reduced greatly and only be O(N). It outperform the local linear regression algorithm and don't need a priori information either.For block fading channel a nonparametric frequecy domain channel estimation algorithm based on 2D kernel smoothing was proposed. According to that the frequency and time domain correlation can be seperated in WSSUS fading channel, a simplified 2D kernel smoothing filter using two cascaded 1D kernel smoothing filters in time domain and frequency domain respectely was adopted to reduce the computaion complexity. Because it can utilize the correlation of the channel gain in time and frequency domain, the estimation performance is better than the above two. If the adaptive kernel window size was employed, the performance can be improved further, and outperforms 1D LMMSE estimator in the low SNR area and the computational complexity is much mo lower.The last part introdused a wavelet block-thresholding channel estimation method. It outperforms the soft threshold wavelet estimator and hard thresholding wavelet estimator because it utilize the correlation of the wavelet detail coefficients. The proposed estimator maintains better performance even when the CP is shorter then the multipath delay spread and with no model mismatch. The computational complexity is only O(N).Through the dissertation we can make a conclution that nonparametric estimation can be well adapted to channel estimation, the derived channel estimation algorithm has better performance while reserving low computational complexity. But there are still many problems, such as the estimation bounds of the estimators, the selection of the windows size of the estimators whitch more adapted to OFDM channel estimation, the smoothing kernel selection of the estimator and the generalization of the nonparametric channel estimation algorithm, to be solved.