A variable step-size incremental LMS solution for low SNR applications

Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. The proposed algorithm has been theoretically analyzed and validated through simulations for different scenarios. Various variable step algorithms for high SNR values exist in the literature.