Analysis of Stability and Performance of Adaptation Algorithms With Time-Invariant Gains

Åhlén, Anders; Lindbom, Lars; Sternad, Mikael

doi:10.1109/tsp.2003.820078

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…It has attained its popularity due to a broad range of useful applications in such diverse areas as communications, radar, sonar, seismology, navigation and control systems, and biomedical electronics. The LMS adaptive filter is very popular due to its simplicity, but even simpler approaches are required for many realtime applications, several different versions of the LMS algorithm have been proposed in the literature [1,2,3,4,5,6]. Reduction of the complexity of the LMS algorithm has received attention in the area of adaptive filters [5,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Modified Clipped LMS Algorithm

Lotfizad

Yazdi

2005

EURASIP J. Adv. Signal Process.

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Abstract

A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( ) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS) algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.

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Section: Introductionmentioning

confidence: 99%

Modified Clipped LMS Algorithm

Lotfizad

Yazdi

2005

EURASIP J. Adv. Signal Process.

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Abstract

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“…For bounded regressors, the small gain theorem [44] provides (conservative) sufficient conditions for stability. Other, less conservative conditions are presented in [30].…”

Section: Stabilitymentioning

confidence: 99%

“…In [30] and [28], three important scenarios are discussed in which an exact stability and performance analysis can be per-formed when assuming , and to be independent. These results are summarized below.…”

Section: Stabilitymentioning

confidence: 99%

“…Under mild assumptions, the stability and performance results can be used as good approximations also for FIR channels of arbitrary order with white circular complex inputs. See [30].…”

Section: ) Fir Channels With White Real or Circular Complex Scalar Imentioning

confidence: 99%

“…7 It is well known for LMS that a large regressor eigenvalue spread will reduce the attainable performance in nonstationary environments. A less known phenomenon, explained in [30], is that the use of binary regressors, instead of Gaussian regressors improves the steady state tracking performance.…”

Section: Example 4: a Performance Comparison For Ar Modelsmentioning

confidence: 99%

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Tracking of time-varying mobile radio channels with WLMS algorithms: a case study on D-AMPS 1900 channels

Sternad

Lindbom²,

Åhlén³

VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026)

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Abstract-Adaptation algorithms with constant gains are designed for tracking smoothly time-varying parameters of linear regression models, in particular channel models occurring in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. The proposed algorithms are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on prior information. The algorithms can be realized as Wiener filters, called Learning Filters, or as "LMS/Newton" updates complemented by filters that provide predictions or smoothing estimates. The simplest algorithm, named the Wiener LMS, is presented here. All parameters are here assumed governed by the same dynamics and the covariance matrix of the regressors is assumed known. The computational complexity is of the same order of magnitude as that of LMS for regressors which are either white or have autoregressive statistics. The tracking performance is, however, substantially improved.

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Steady-state analysis of the long LMS adaptive filter

Butterweck

2011

Signal Processing

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Analysis of Stability and Performance of Adaptation Algorithms With Time-Invariant Gains

Cited by 13 publications

References 32 publications

Modified Clipped LMS Algorithm

Modified Clipped LMS Algorithm

Tracking of time-varying mobile radio channels with WLMS algorithms: a case study on D-AMPS 1900 channels

Steady-state analysis of the long LMS adaptive filter

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