Dynamic convergence analysis of linear adaptive filters for narrowband interference suppression in spread spectrum systems

A linear adaptive filter is one of the simplest methods to reject narrowband interference in spread spectrum systems and yields a significant increase in the received signal-to-noise ratio (SNR). In many scenarios, the interference is time varying. Therefore, characterizing the ability of the adaptive filter to track dynamically the interferer is of great interest. In this, paper, we study in parallel the dynamic convergence behaviors of the classical linear predictor and linear interpolator adapted by the LMS and normalized LMS algorithms using two analytical methods: the independence method and the small step-size method. We verify our theoretical MSE trajectories using computer simulations.