Universal non-data-aided small-sample-support adaptive spread-spectrum receivers

Abstract

We revisit the problem of recovering a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive Gaussian or impulsive noise. The general receiver structure is composed of an adaptive chip-based nonlinear pre-processor followed by an adaptive linear filter. We propose two blind algorithms for the adaptation of the parameters of the nonlinear pre-processor that are based on the Fisher discriminant function evaluated at the linear filter output. The first algorithm couples itself with the adaptation of the linear post-processor, while the second algorithm is a decoupled procedure that is independent of the linear post-processor. Both algorithms lead to universal receiver solutions in the sense that they mitigate a wide range of additive noise effects (from purely Gaussian to impulsive) and correlated SS interference. Both algorithms offer superior detection performance when receiver adaptation is carried out with limited input data observations