Nonlinear Techniques for Interference Suppression in Spread-Spectrum Systems

It is known that the narrow-band interference rejection capability of a direct-sequence spread-spectrum system can be enhanced considerably by passing the received signal through a prediction error filter prior to correlating it with the PN sequence. Previous work on this problem has centered around the use of linear prediction filiters for this purpose. In this paper, the binary nature of the direct-sequence signals is exploited to obtain nonlinear filiters that outperform the linear filters hitherto employed for this purpose. The case of a Gaussian interferer with known autoregressive parameters is considered first. Using simulations, it is shown that an approximate conditional mean (ACM) filter of the Masreliez type performs significantly better than the optimum linear (Kalman-Bucy) filter. For the case of interferers with unknown parameters, the nature of the nonlinearity in the ACM filter is used to obtain an adaptive filtering algorithm that is identical to the linear transversal filter except that the previous prediction errors are transformed nonlinearly before being incorporated into the linear prediction. Two versions of this filter are considered, one in which the filter coefficients are updated using the Widrow LMS algorithm, and another in which the coefficients are updated using an approximate gradient algorithm. Simulations indicate that the nonlinear filter with LMS updates performs substantially better than the linear filter for both narrow-band Gaussian and single-tone interferers, whereas the gradient algorithm gives slightly better performance for Gaussian interferers but is rather ineffective in suppressing a sinusoidal interferer.