Convergence Analysis of Proportionate-type Least Mean Square Algorithms
Vinay Chakravarthi Gogineni, Subrahmanyam Mula
TL;DR
This paper provides a detailed convergence analysis of the proportionate-type LMS algorithm, demonstrating its effectiveness for sparse system identification and real-time VLSI applications through theoretical and simulation results.
Contribution
It offers the first comprehensive first and second order convergence analysis of the Pt-LMS algorithm, highlighting its optimal convergence behavior.
Findings
Proposed convergence analysis aligns with simulation results.
Pt-LMS effectively identifies sparse systems.
Suitable for real-time VLSI implementations.
Abstract
In this paper, we present the convergence analysis of proportionate-type least mean square (Pt-LMS) algorithm that identifies the sparse system effectively and more suitable for real time VLSI applications. Both first and second order convergence analysis of Pt-LMS algorithm is studied. Optimum convergence behavior of Pt-LMS algorithm is studied from the second order convergence analysis provided in this paper. Simulation results were conducted to verify the analytical results.
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Taxonomy
TopicsAdvanced Adaptive Filtering Techniques · Speech and Audio Processing · Blind Source Separation Techniques