Variable Step Size Affine Projection Algorithm for Adaptive Multiuser DS-CDMA MMSE Receiver
Wireless Personal Communications: An International Journal
Detection-guided fast affine projection channel estimator for speech applications
EURASIP Journal on Audio, Speech, and Music Processing
EURASIP Journal on Applied Signal Processing
Subband affine projection algorithm for acoustic echo cancellation system
EURASIP Journal on Applied Signal Processing
Derivation of Excess Mean-Square Error for Affine Projection Algorithms Using the Condition Number
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A stochastic model for a pseudo affine projection algorithm
IEEE Transactions on Signal Processing
An affine projection algorithm using the inner product of input vectors
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Fast exact variable order affine projection algorithm
Signal Processing
Affine projection algorithm with selective projections
Signal Processing
An affine projection algorithm with variable step size and projection order
Digital Signal Processing
Hi-index | 35.69 |
A class of equivalent algorithms that accelerate the convergence of the normalized LMS (NLMS) algorithm, especially for colored inputs, has previously been discovered independently. The affine projection algorithm (APA) is the earliest and most popular algorithm in this class that inherits its name. The usual APA algorithms update weight estimates on the basis of multiple, unit delayed, input signal vectors. We analyze the convergence behavior of the generalized APA class of algorithms (allowing for arbitrary delay between input vectors) using a simple model for the input signal vectors. Conditions for convergence of the APA class are derived. It is shown that the convergence rate is exponential and that it improves as the number of input signal vectors used for adaptation is increased. However, the rate of improvement in performance (time-to-steady-state) diminishes as the number of input signal vectors increases. For a given convergence rate, APA algorithms are shown to exhibit less misadjustment (steady-state error) than NLMS. Simulation results are provided to corroborate the analytical results