Matrix theory: a second course
Matrix theory: a second course
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Computational methods of signal recovery and recognition
Computational methods of signal recovery and recognition
Subspace-Based Schemes for NBI and MAI Suppression in DS-CDMA Communications
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
A regularized LMS technique is presented that uses a modifiedoptimality criterion which enhances the detection capabilities of directsequence spread spectrum systems. The rejection filter is updated based uponan additional regularization input which limits the self-noise of thefilter, especially at moderate signal-to-interference power ratios. Theregularization is controlled by a single scalar parameter, that can bevaried to produce the optimal Wiener filter weights or the decision-feedbackfilter weights. An advantage of the regularized filter is that the weighterror surface is quadratic, leading to well behaved convergence propertiesfor adaptive implementations. Simulation results are presented which comparethe regularized filter to the optimal Wiener filter and thedecision-feedback filter.