A Variable Step-Size NLMS Algorithm with Adaptive Coefficient Vector Reusing

Resende, Leonardo C.; Haddad, Diego B.; Petraglia, M.R.

doi:10.1109/eit.2018.8500096

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…Typically, algorithms with coefficients reuse adopt a normalized update term, giving rise to the RC-NLMS algorithm and its variants, which can present a reuse factor and/or a time-varying learning factor [30,31]. More recently, reference [32] detailed how to obtain a non-normalized algorithm that employs the RC technique.…”

Section: Reuse Coefficients Techniquementioning

confidence: 99%

“…It is known that the MSD has a tendency to increase when the noise variance is large [8,33]. This impact is usually overcome by the adoption of coefficient reuse (RC) [31]. The main objective is to propose the minimization of the weighted sum of the square Euclidean norm of the difference between the updated coefficient vector and the L vectors of previous coefficients.…”

Section: Reuse Coefficients Techniquementioning

confidence: 99%

“…This equation describes the update of the proposed RC-LMS algorithm (in the ATC modality). When the measurement noise presents high energy, the oscillations of the adaptive estimator contribute to increase the MSD, which can be critical in several applications [31]. In this case, the insertion of the coefficients reuse (as described in (52)) can improve the performance in the permanent regime.…”

Section: Proposal Of An Atc Algorithm With Reuse Of Coefficientsmentioning

confidence: 99%

“…An undesirable feature of the RC-LMS algorithm is the reduction of the convergence rate [53]. Reference [30] compensated this behavior between the MSD and the convergence rate through the dynamic adjustment of the reuse order L(k), which can be incorporated into the diffuse context as follows:…”

Section: Vrc-lms Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Adaptive Filtering on Wireless Sensor Networks with Shared Medium Competition

Carmo¹,

Tarrataca²,

Colares³

et al. 2020

Learn. Nonlin. Mod.

Self Cite

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Wireless Sensor Networks (WSN) are of significant importance with increasingly diverse and viable applications. They gained even more traction after the IEEE 802.15.4 standard was defined. Distributed adaptive filtering algorithms have added statistical inference to WSN applications, employing techniques that extract data from distributed devices. In contrast, most adaptive filtering contributions do not consider realistic features of the subjacent telecommunications network protocols. Similarly, the telecommunications area typically does not take into account interesting abilities of adaptive filtering algorithms. In this paper, we explore this gap between the two study areas, allowing the development of network-protocol-aware distributed adaptive filtering techniques. In order to explore network realistic behaviors, this paper focuses on distributed inference problems. More specifically, we propose two new diffuse adaptive algorithms, aware of the characteristics of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, namely: (i) Variant Reuse of Coefficients Least Mean Squares (VRC-LMS) algorithm; and the (ii) Reuse of Coefficients Least Mean Squares (RC-LMS) algorithm in the Adapt-Then-Combine (ATC) modality. These two new algorithms will bring some advantages, specifically when information is delayed because of too much packet loss. Another advantage will be the addition of the spatial information diversity contribution in the VRC-LMS algorithm.

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Section: Reuse Coefficients Techniquementioning

confidence: 99%

Section: Reuse Coefficients Techniquementioning

confidence: 99%

Section: Proposal Of An Atc Algorithm With Reuse Of Coefficientsmentioning

confidence: 99%

Section: Vrc-lms Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Adaptive Filtering on Wireless Sensor Networks with Shared Medium Competition

Carmo¹,

Tarrataca²,

Colares³

et al. 2020

Learn. Nonlin. Mod.

Self Cite

View full text Add to dashboard Cite

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“…The computational effort involved in the coefficient reuse strategy may be reduced using the Set-Membership approach [18]. Furthermore, the trade-off between convergence rate and steady-state behavior can be relaxed through a time-variant coefficient reuse factor [16], [19].…”

Section: Rc-nlms Algorithmmentioning

confidence: 99%

The ℓ0-Norm Constraint Coefficient Reusing Least Mean Square Algorithm

Resende¹,

Leonardo²,

Siqueira³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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An adaptive filtering algorithm should present fast convergence and good steady-state behaviour. Both metrics can be enhanced if one makes use of the sparsity (energy concentration in few coefficients) of the involved (unknown) transfer function. Unfortunately, such an approach can suffer from steady-state performance degradation when the signal-to-noise ratio is low, due to an increase of the adaptive weights variance. This work advances a new algorithm that combines the reusing coefficient strategy, which increases the robustness against the additive noise, with a sparsity-aware algorithm, which employs an approximation of the ℓ0-norm in order to penalize non-sparse adaptive vector estimations.

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