Blind and semi-blind equalization:: methods and algorithms

Channel identification techniques that do not require the use of a training sequence (blind methods), or that con operate with ver short training sequence (semi-blind methods) are a topic of major concern for modern communication applications. This paper presents a review of channel identification methods that are applicable in this context, with a strong emphasis on second-order subspace-based and maximum likelihood (ML) estimation schemes. The main focus of the paper is on: (i) providing a clear picture of the principle and theory associated with subspace-based methods in the blind and semi-blind contexts; (ii) describing algorithmic solutions, sometimes based on novel results, that are suitable for carrying out the delicate likelihood optimization task associated with ML estimation.