Stochastic Newtons-like multi modulus blind equalization algorithm

Abstract

In this thesis, a new stochastic Newton-like algorithm for blind equalization is developed by applying a complex Taylor series on a recently proposed soft constraint satisfaction multi-modulus blind equalization algorithm of order p (SCS-MMA-p). The proposed algorithm is a generalized version of an existing stochastic Newton-hike soft constraint satisfaction multi-modulus algorithm and is named as stochastic Newton-like soft constraint satisfaction multi-modulus algorithm of order p (SNL-SCS-MMA-p). The performance of the proposed algorithm is investigated under different scenarios for different values of p. Moreover, a lattice structure for pre-orthogonalization is used to enhance more the convergence speed of the algorithm.