Maximum likelihood based identification for nonlinear multichannel communications systems

Abstract

Nonlinear distortions are important issues in many communications systems. Therefore, this paper deals with the blind and semi-blind identification of nonlinear SIMO/MIMO channels. Quadratic and cubic nonlinearities are considered for the system model as well as a discussion on how the developed work can be extended to more general nonlinear models. The proposed blind solution is initialized by using a subspace approach, which is followed by an appropriate ambiguity removal method, then refined by a Maximum Likelihood (ML) based processing using the Expectation-Maximization (EM) algorithm. The proposed semi-bind solution, involving both data and pilots, is fully based on the EM algorithm. These solutions are supported by some identifiability results and performance bounds analysis related to the considered models (blind and semi-blind). Finally, simulation results essentially show that the proposed algorithms exhibit very attractive channel estimation performance, with interesting convergence speed for the EM-based iterative processing.