Proportionate-type normalized least mean square (PtNLMS) algorithms are developed and analyzed. PtNLMS algorithms offer low computational complexity and fast convergence times, for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined.
Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization.
Miloš Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications.
Chapter 1: Introduction to PtNLMS Algorithms and Their Analysis
Chapter 2: LMS Analysis Techniques
Chapter 3: PtNLMS Analysis Techniques
Chapter 4: Algorithms Designed based on Minimization of User Defined Criteria
Chapter 5: Probability Density of Weight Deviations for PtLMS Algorithms
Chapter 6: Adaptive Step-size PtNLMS Algorithms
Chapter 7: Complex PtNLMS Algorithms
Chapter 8: Computational Complexity for PtNLMS Algorithms