A Proportionate Fast NLMS with Competent Individual Activation Factors

This study presents the CIAF-PFNLMS algorithm, an abbreviation for Proportionate Fast Normalized Least Mean Square with Competent Individual Activation Factors. This algorithm provides a competent method for calculating individual activation factors. This guarantees that the gain remains proportional to its associated coefficient even when the coefficient is inactive, in accordance with the concept of proportionality. This proves very beneficial in scenarios involving sparse impulse responses, leading to significant performance improvements. Additionally, the algorithm smoothly integrates the Fast Normalized Least Mean Square (FNLMS) adaptation gain into its framework, making it effective in environments with dispersive impulse responses and capable of accurately tracking echo path variations. In our study, we performed simulations to validate the capabilities of the proposed algorithm. We evaluated its performance across a range of scenarios, including sparse and dispersive environments, non-stationary situations, and compared it with established algorithms.