An efficient Recursive Inverse adaptive filtering algorithm for channel equalization

The recently proposed Recursive Inverse (RI) algorithm was shown to have a similar mean-square-error (mse) performance as the Recursive-Least-Squares (RLS) algorithm with reduced complexity. The selection of the forgetting factor has a significant influence on the performance of the RLS algorithm. The value of the forgetting factor leads to a tradeoff between the stability and the tracking ability. In a system identification setting, both the filter length and a leakage phenomenon affect the selection of the forgetting factor. In this paper, we first analytically show that this leakage phenomenon and the filter length have much less influence on the performance of the RI algorithm. Simulation results, in a system identification setting, validate the theoretical results.

show abstract

“…In the case of the RI algorithm, (5) and (6) are replaced by the recursive estimates of the instantaneous correlations.…”

Section: B System Identification -Ri Algorithmmentioning

confidence: 99%

“…In this paper, we analyze this leakage phenomenon for the recently proposed RI algorithm [5], and give a theoretical estimation of it in terms of the forgetting factor β and the filter length N . These findings will be compared with those of the RLS algorithm introduced in [6].…”

Section: Introductionmentioning

confidence: 99%

The effect of the forgetting factor on the RI adaptive algorithm in system identification

Ahmad

Kükrer

Hocanin

2011

ISSCS 2011 - International Symposium on Signals, Circuits and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to under water acoustic noises, man-made noise, atmospheric noises, etc., noise process is better to be modeled as impulsive rather than Gaussian noise [9], [10]. An impulsive noise can be generated using the probability density function: were selected.…”

Section: ) Additive White Impulsive Noisementioning

confidence: 99%

A Modified Leaky-LMS Algorithm

Tajuddeen¹,

Salman²,

Abuhilal³

2014

IJCEE

View full text Add to dashboard Cite

Abstract-The leaky least-mean-square (LLMS) algorithm was first proposed to mitigate the drifting problem of the leastmean-square (LMS) algorithm. Though the LLMS algorithm solves this problem, its performance is similar to that of the LMS algorithm. In this paper, we propose an improved version of the LLMS algorithm that brings better performance to the LLMS algorithm and similarly solves the problem of drifting in the LMS algorithm. This better performance is achieved at a negligible increase in the computational complexity. The performance of the proposed algorithm is compared to that of the conventional LLMS algorithm in a system identification and a noise cancellation settings in additive white and correlated, Gaussian and impulsive, noise environments. IndexTerms-Leaky least-mean-square, system identification, noise cancellation. I. INTRODUCTIONThe least-mean-square (LMS) algorithm [1] is one of the most famous adaptive filtering algorithms because of its simplicity and ease of analysis. This has made most researchers to improve the LMS algorithm and also to find solutions to some of its drawbacks. Some of these improved algorithms include: the normalized least-mean-square (NLMS) [2], variable step-size least-mean-square (VSSLMS) [3], etc. These improved algorithms generally improve the performance of the LMS algorithm in terms of convergence rate and mean-square-error (mse) value.One of the main drawbacks of the LMS algorithm is the drifting problem as analyzed in [4]. This is a situation where the LMS algorithm generates unbounded parameter estimates for a bounded input sequence. This may drive the LMS weight update to diverge as a result of inadequate input sequence [4]. The drifting problem has been shown in [5]-[7] in details.The leaky least-mean-square (LLMS) algorithm is one of the improved LMS-based algorithms that use a leakage factor to control the weight update of the LMS algorithm [5], [6]. This leakage factor solves the problem of drifting in the LMS algorithm by bounding the parameter estimate. It also improves the tracking capability of the algorithm, convergence and stability of the LMS algorithm.One of the main drawbacks of the LLMS algorithm is its low convergence rate compared to the other improved LMSbased algorithms. In this paper, we propose a new algorithm that improves the convergence rate of the LLMS algorithm. This is achieved by employing the sum of exponentials of the error as the cost function; this cost function is a generalized of the stochastic gradient algorithm as proposed by Boukis et al. [8]. A leakage factor is added to the sum of exponential cost function which makes the proposed algorithm a combination of the generalized of the mixednorm stochastic gradient algorithm with a leaky factor. This paper is organized as follows. In Section II, a review of the LLMS is introduced. In Section III, the proposed algorithm is introduced. In Section IV, experimental results are presented and discussed. Finally, the conclusions are drawn. II. LEAKY LEAST MEAN SQUARE ALGORITHMIn sy...

show abstract

“…However, the RLS algorithm suffers from its high computational complexity, stability problems when the forgetting factor is relatively low, and tracking capability when the forgetting factor is relatively high. The RI algorithm [1,2], was recently proposed to overcome these problems. It is also known that the RLS and RI algorithms both provide a poor performance in impulsive-noise environments when the SNR is low.…”

mentioning

confidence: 99%

“…In this paper, we propose a new RI algorithm that is robust to impulsive noise and provides better MSE performance than the recently proposed Robust RLS algorithm [4], with a considerable reduction in the computational complexity [1,2] as shown in Table 1. The proposed method employs the technique introduced in [4].…”

mentioning

confidence: 99%

Robust Recursive Inverse Adaptive Algorithm in Impulsive Noise

Ahmad

Kükrer

Hocanin

2011

Circuits Syst Signal Process

Self Cite

View full text Add to dashboard Cite

The recently proposed Recursive Inverse (RI) algorithm has shown a significant performance improvement compared to that of the Recursive Least Squares (RLS) algorithm, in various noise environments. However, both algorithms fail to converge in certain impulsive noise environments, especially if the Signal-to-Noise Ratio (SNR) is low. In this paper, a Robust RI algorithm is proposed. Analytical results show that robustness against impulsive noise is achieved by choosing the weights on the basis of the L 1 norms of the autocorrelation matrix and the crosscorrelation vector. Simulation results confirm that the proposed algorithm provides an improved performance, with a reduction in computational complexity, compared to those of the RLS and the Robust RLS in white and correlated impulsive noise.

show abstract

An efficient Recursive Inverse adaptive filtering algorithm for channel equalization

Cited by 9 publications

References 11 publications

The effect of the forgetting factor on the RI adaptive algorithm in system identification

The effect of the forgetting factor on the RI adaptive algorithm in system identification

A Modified Leaky-LMS Algorithm

Robust Recursive Inverse Adaptive Algorithm in Impulsive Noise

Contact Info

Product

Resources

About