Research On Blind Channel Estimation Based On Noise And Oblique Projection Subspace For MIMO-OFDM Systems

Orthogonal Frequency Division Multiplexing System(OFDM) has a distinctivefeature of string conversion and orthogonality between subcarriers. These featuresmake it possible to combat frequency selecting fading effectively, and to improvethe spectral efficiency and transmission speed. Multiple Input Multiple OutputSystem(MIMO) can be summarized as diversity and multiplexing systems, whichcan improve the transmission reliability and system capacity. By the combination ofOFDM and MIMO, MIMO-OFDM system can satisfiy the technical requirements of4G communications. In order to protect the communication quality and goodperformance of the receiver design, channel estimation is an important part ofmodern wireless communication technology.In this paper, based on MIMO-OFDM systems, we introduce the developmentand technology of blind channel estimation, focusing on two kinds of blind channelestimation algorithm, the noise subspace and oblique projection subspace. Channelestimation of MIMO-OFDM system can be classified estimation based on the pilotor training sequence, blind channel estimation and semi-blind channel estimation.Blind channel estimation using the inherent characteristics of the received signal,such as the sub-space and cirulation smooth and so on, has good performance with awide range of applications, good stability, bandwidth utilization, high efficiency.Blind channel estimation can be divided into higher-order statistics(HOS) andsecond-order statistics(SOS). SOS, including subspces algorithms and maximumlikehood algorithm, has a wide range of concerns because of its fast convergencespeed, easy to implement algorithms structure and convenient.In this paper, MIMO-OFDM system is proposed with two algorithms, based onnoise subspace and oblique projection algotithms. The autocorrelation function ofreceived signal in the noise subspace algotithm is experienced an SVDdecomposition. The noise subspace is orthogonal to signal subspace and channelsubspce respectively. As a consequence of that, we can consturcted the cost function.The minimum value of the function is corresponding to the channel parameterinformation integrity. In the oblique projection subspace, the spatial stuucture of thereceived signal is divided into three parts, past subspace, future subspace andpresent subspace. The present subspace contains the complete channel paraments,and the past and future subspace is equivalent to the interference. Oblique projectioncontains incomplete channel paraments. In this paper, we conduct a combination ofthe two oblique projection which include complete channel paraments. One methodis to construct a special matrix, the rank of which is one, the other one is to add the oblique projections directly. By simulation and theoretical analysis, the performanceof the noise subspace is more superior than the oblique projection subspace. But thecalculation of the noise subspace is more complicated. The second algorithm ofoblique projection subspace performs better than the first one.