Study For Blind Channel Identification And Equalization Algorithm Based On Second-Order Statistics

As in most communication systems such as mobile communication systems , channel is unknown and time-charged. So it's necessary for us to study the equalization not depend on the training serials, but on the received signals only, which called blind equalization technique. Similarly, the methods needing no training serials are celled blind methods.Traditionally, blind channel identification and equalization are all based on high order statistics. In the 1990's, the method of blind channel identification using only second-order statistics has be on proposed, and it's a major breakthrough. Due to over-sampling, the equivalent channel matrix possesses a particular structure which enables us to estimate the channel. In this paper we study the time domain approach for blind identification and equalization, which are based on second-order statistics. In the research, lots of work of simulation is concerned. Over-sampling the received signals will product the cyclostationarity of communication signals, therefore, an equivalent SIMO (Single Input Multiple Output) model of SISO (Single Input Single Output) system is proposed. Basing on the equivalent SIMO system model, we developed a new algorithm for blind channel identification and equalization of possibly non-minimum phase channels using only second-order statistics.