A Study Of Subspace Blind Equalization Algorithm Based On Second-Order Statistics

Blind equalization is a new adaptive technology without resorting to a training sequence, which only utilizes the prior information of transmitted signals to equalize the channel character, and makes sure that the output sequence approximates the transmitted signals as accurate as possible. Cyclostationarity theory is the signal processing method witch is situated between the non-stationary signal and the stationary signal, it reflects the changes of the signal statistics while the time is changing, makes up for the lack of stationary signal processing, on the other hand, the general non-stationary signal processing is simple after used the cyclical of the signal statistics.The second-order statistics not only contain the amplitude information, but also contain the phase information. So, the blind equalization can be achieved non-minimum phase system identification if it is introduced cyclostationarity theory. The signals of channel output which have been oversampling are cyclostationarity and the channel can be equalized utilizing the second-order statistics of signals. Sampling data and computation are smaller than that using higher-order statistics, and ISI can be effectively eliminated in the mobile communications channel. It is very important theoretical significance and practical value not only in the blind equalization but also be applied to many other area.The major contribution of this paper is summarized as follow:(1) This paper analyses the blind equalization rulers, introduces the principle of the second-order statistics. The blind equalization algorithm based on the second-order statistics is summed up. It is clarified that the application of the blind equalization algorithm based on second-order statistics is necessity and feasibility.(2) This paper sets up Single-input Multiple-output (SIMO) model and describes the subspace method of SIMO systems. The process of signal transmission through the equalizer is simulated and the Bit Error Rate (BER) performance of the equalizer is theoretically analyzed. This method can identify the transfer function of the system only using SOS by sampling multiple outputs or oversampling a single output. It lays an important foundation for blind equalization and blind identification.(3) A new subspace blind equalization algorithm based on cyclostationary theory and second-order statistics is presented. The process of the new algorithm is simulated by computer and verified the superiority of the new algorithm. (4) Two main types of linear blind equalization algorithm are detailed descripted: linear prediction algorithm and outer-product decomposition algorithm. The comparisons with the new algorithm are shown through computer simulations. Then it is found that the proposed algorithm has the advantages in low complexity, and will not drop into local optimization. What's more, the algorithm attains an excellent performance of the symbol error.