Blind System Equalization And Identification Based On Higher Order Statistics

In this paper, the problem of blind identification and blind equalization of digital communication systems based on the higher order statistics (HOS) is addressed. Based on subspace analysis, we develop two improved algorithms of multiple-input multiple-output (MIMO) systems blind identification and blind equalization.Firstly, this paper introduces the development and research results of blind equalization and blind identification of linear systems with finite impulse response. Then we introduce the definition and algebraic properties of higher order statistics.This paper introduces two algorithms. One algorithm is about blind equalization which observe the output of an unknown possibly nonminimum phase linear system from which the algorithm want to recover its input using an adjustable linear filter. The other algorithm is about blind identification of multiple-input multiple-output linear systems with finite impulse response. This algorithm utilizes the common nullspace of a set of fourth-order cumulant matrices to identify an unknown MIMO channel impulse response up to a constant monomial matrix. This paper alse introduces a set of new identifiability conditions for the algorithm. It is shown that for MIMO systems with an equal channel impulse length for different users, the algorithm requires a weaker identifiability condition. At the same time, this paper gives some simulation examples to illustrate the performance of the two algorithms.This paper mainly studies the algorithms of multiple-input multiple-output (MIMO) systems blind identification and blind equalization. According to the disadvantage of MIMICS algorithm, which not makes the most of inherent structure of cumulant matrix, this contribution develop two improved algorithms and improve its performance of estimation. Computer simulations can prove the two improved algorithms effectiveness.