A fast convergence algorithm for sparse-tap adaptive FIR filters identifying an unknown number of dispersive regions

This paper proposes a fast convergence algorithm for sparse-tap adaptive finite impulse response (FIR) filters to identify an unknown number of multiple dispersive regions. Coefficient values and tap-positions of the adaptive filter are simultaneously controlled. A constrained region for new-tap positions is selected from equisize subgroups of all possible tap-positions, and it hops from one subgroup to another to cover multiple dispersive regions. The hopping order and the stay time for each subgroup are adaptively determined based on the absolute coefficient values. Simulation results with colored signals show that the proposed algorithm saves more than 80% in the convergence time over the full-tap NLMS and 50% over the STWQ. Tracking capability of the proposed algorithm exhibits its superior characteristics. These characteristics are confirmed by hardware evaluations with a telephone network simulator.