A linearly constrained blind equalization scheme based on Bussgang type algorithms

Abstract

Existing blind adaptive equalizers that use nonconvex cost functions (as Bussgang type algorithms) and stochastic gradient descent suffer from lack of global convergence to an equalizer tap set that removes sufficient ISI when an FIR equalizer is used. In this paper we propose a new algorithm including tap anchoring and gain recovery into the classical schemes. The combined effect of these strategies is to establish the preservation of the transmitted symbol preventing ill convergence, and therefore providing the ability of implementation of the inverse filter regardless of the initial ISI. Under certain hypotheses, we suggest that a globally convex scheme can be proposed overcoming the existing structures. Several computer simulations support our theoretical results.