A Regularized LMS Algorithm for Narrowband Interference Rejection in Direct Sequence Spread Spectrum Communications

A regularized LMS technique is presented that uses a modified optimality criterion which enhances the detection capabilities of direct sequence spread spectrum systems. The rejection filter is updated based upon an additional regularization input which limits the self-noise of the filter, especially at moderate signal-to-interference power ratios. The regularization is controlled by a single scalar parameter, that can be varied to produce the optimal Wiener filter weights or the decision-feedback filter weights. An advantage of the regularized filter is that the weight error surface is quadratic, leading to well behaved convergence properties for adaptive implementations. Simulation results are presented which compare the regularized filter to the optimal Wiener filter and the decision-feedback filter.