Performance Analysis of the Deficient Length EDS Adaptive Algorithm

Zhang, Zhiqing Philippe; Bose, T.; Xiao, Li; Thamvichai, Ratchaneekorn

doi:10.1109/apccas.2006.342372

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Since the deficient-length LMS presents a learning behavior for correlated inputs that is distinct from that when the input signal is white [23,43], the analysis put forth in this paper is not restricted to an uncorrelated excitation sequence x(k).…”

Section: Lms Algorithmmentioning

confidence: 99%

Exact expectation analysis of the deficient-length LMS algorithm

2019

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Stochastic models that predict adaptive filtering algorithms performance usually employ several assumptions in order to simplify the analysis. Although these simplifications facilitate the recursive update of the statistical quantities of interest, they by themselves may hamper the modeling accuracy. This paper simultaneously avoids for the first time the employment of two ubiquitous assumptions often adopted in the analysis of the least mean squares algorithm.The first of them is the so-called independence assumption, which presumes statistical independence between adaptive coefficients and input data. The second one assumes a sufficient-order configuration, in which the lengths of the unknown plant and the adaptive filter are equal. State equations that characterize both the mean and mean square performance of the deficient-length configuration without using the independence assumption are provided. The devised analysis, encompassing both transient and steady-state regimes, is not restricted neither to white nor to Gaussian input signals and is able to provide a proper step size upper bound that guarantees stability.

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Section: Lms Algorithmmentioning

confidence: 99%

Exact expectation analysis of the deficient-length LMS algorithm

2019

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“…The convergence performance and mean-square performance of different partial update CG algorithms are compared in a system identification application. The system identification model is shown in Figure 1 and is taken from (Zhang et al, 2006). The unknown system (Mayyas, 2005) is a 16-order FIR filter (N=16), with impulse response [0.01, 0.02, −0.04, −0.08, 0.15, −0.3, 0.45, 0.6, 0.6, 0.45, −0.3, 0.15, −0.08, −0.04, 0.02, 0.01] T .…”

Section: Performance Of Different Pu Cg Algorithmsmentioning

confidence: 99%

“…Adaptive filter Figure 1: System identification model. The correlated input of the system (Zhang et al, 2006) has the following form…”

Section: Performance Of Different Pu Cg Algorithmsmentioning

confidence: 99%

Partial Update Conjugate Gradient Algorithms for Adaptive Filtering

Xie

Bose

2011

Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems

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In practice, computational complexity is an important consideration of an adaptive signal processing system. A well-known approach to controlling computational complexity is applying partial update (PU) adaptive filters. In this paper, a partial update conjugate gradient (CG) algorithm is employed. Theoretical analyses of mean and mean-square performance are presented. The simulation results of different PU CG algorithms are shown. The performance of PU CG algorithms are also compared with PU recursive least squares (RLS) and PU Euclidean direction search (EDS) algorithms.

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“…If the filter length is too long compared to the channel spread, the convergence rate slows down. If the filter length is deficient, the filter algorithm may diverge [12,13].…”

mentioning

confidence: 98%

Dynamically Partitioned Hierarchical Constant Modulus Algorithm for Variable Delay Spread Channels

Woo

2010

Wireless Pers Commun

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The Constant Modulus Algorithm (CMA) adopts the constant modulus (CM) criterion to minimize the deviation of the equalizer output from a fixed value. However it has two drawbacks: (1) slow rate of convergence (2) the likelihood of getting trapped in a local minimum. The problem gets even worse when the channel delay spread varies rapidly as the filter length cannot match the delay spread. If the filter length is significantly longer than the delay spread, the convergence rate can be slow. In this paper, we improve the performance of the standard CMA by using a dynamically partitioned hierarchical structure to organize the taps of a filter. The filter length is dynamically partitioned according to the delay spread such that they are tightly matched. Preferably, the length of a sub-filter is slightly longer than the delay spread of the channel. The performance evaluation is divided into two parts. In Part I, we do simulation runs for both the cost functions and cost surfaces comparing the standard CMA, where the filter length is significantly longer than the delay spread, and the dynamically partitioned CMA, where the filter length matches the delay spread. In Part II, an analysis is provided to show that the proposed partitioned scheme can speed up the convergence rate and reduce the cost function.

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Performance Analysis of the Deficient Length EDS Adaptive Algorithm

Cited by 9 publications

References 4 publications

Exact expectation analysis of the deficient-length LMS algorithm

Exact expectation analysis of the deficient-length LMS algorithm

Partial Update Conjugate Gradient Algorithms for Adaptive Filtering

Dynamically Partitioned Hierarchical Constant Modulus Algorithm for Variable Delay Spread Channels

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