Multiple environment optimal update profiling for steepest descent algorithms

Methods for use of prior information about multiple operating environments, in improving adaptive filter convergence properties are discussed. More concretely, the gain selection, profiling and scheduling in steepest descent algorithms are treated in detail. The work presented is an extension of Milisavljevic (2000). Two flavors of optimization are discussed: average descent rate optimization and maximization of the minimum descent rate. It is demonstrated, just as in the case of single channel optimization, with no additional complexity a substantial increase of convergence rate of steepest descent algorithms can be achieved. Finally, performance of the method is analyzed on the adaptive linear equalizer design for local area networks.