Sparsity-Aware Data-Selective Adaptive Filters

Lima, Markus V. S.; Ferreira, Tadeu N.; Martins, Wallace A.; Diniz, Paulo S. R.

doi:10.1109/tsp.2014.2334560

Cited by 85 publications

(69 citation statements)

References 34 publications

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“…The zero attractor (ZA) algorithms are obtained by choosing p as the l 1 norm leading to ∇p(w(k)) = sign(w(k)). 2 An interesting alternative is to choose p = F β [18], where F β is an approximation to the l 0 "norm" and the parameter β controls the tradeoff between smoothness and accuracy of the approximation. There are many suitable functions F β -see [18], [19]-, but for the simulations presented in the following section we consider just the Geman-McClure function (GMF), which leads to (18) where (∇p(w(k))) n represents the n th entry of the gradient vector.…”

Section: Penalty-based Algorithmsmentioning

confidence: 99%

“…In [19], it is shown that, in some situations, the term αx(k)S (k)x H (k) may be discarded with no harm to the convergence, which gives rise to the quasi NLMS-SSI (QNLMS-SSI). Another alternative, which is related to the approximation to the l 0 "norm" [18], is to choose (∇p(w(k))) n = (sign(w n (k)))/(1 + |w n (k)|) with small positive , which is known as reweighted ZA-NLMS (RZA-NLMS) algorithm [20]. 3 The SM versions can be derived by considering γ = 0 and γ(k) = γsign(e(k)).…”

Section: Penalty-based Algorithmsmentioning

confidence: 99%

“…In this way, the sparse SM-NLMS (SSM-NLMS) is obtained when p = F β , as in (18) for example. Moreover, when the term αx(k)S (k)x H (k) is discarded, the SSM quasi NLMS (SSM-QNLMS) is unveiled [18]. 4 …”

Section: Penalty-based Algorithmsmentioning

confidence: 99%

“…Advantages of the l 0 "norm" penalty over the l 1 norm are addressed in [18], [23], [19]. The complexity of some of these algorithms can be found in [18], including the SM algorithms, which reduce computational cost by not updating the filter coefficients at every iteration.…”

Section: E Additional Topicsmentioning

confidence: 99%

“…The complexity of some of these algorithms can be found in [18], including the SM algorithms, which reduce computational cost by not updating the filter coefficients at every iteration.…”

Section: E Additional Topicsmentioning

confidence: 99%

See 4 more Smart Citations

Performance evaluation of adaptive filters for sparse wireless channel estimation

Lima

Ferreira

Martins

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The progressive increase of data rates in wireless communication systems has induced channel models with sampled impulse responses which are mostly sparse. This paper presents a unified derivation of adaptive filters exploiting sparsity in the complex domain, and compares the performance of classic and state-of-the-art adaptive algorithms for estimating sparse wireless channels as well as their tracking ability in this inherently time-varying environment. Simulation results confirm the efficiency of the sparsity-aware algorithms.

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Section: Penalty-based Algorithmsmentioning

confidence: 99%

Section: Penalty-based Algorithmsmentioning

confidence: 99%

Section: Penalty-based Algorithmsmentioning

confidence: 99%

Section: E Additional Topicsmentioning

confidence: 99%

“…The complexity of some of these algorithms can be found in [18], including the SM algorithms, which reduce computational cost by not updating the filter coefficients at every iteration.…”

Section: E Additional Topicsmentioning

confidence: 99%

See 3 more Smart Citations

Performance evaluation of adaptive filters for sparse wireless channel estimation

Lima

Ferreira

Martins

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Improved feature least mean square algorithm

Yazdanpanah

2022

Adaptive Control & Signal

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In this paper, we propose the improved feature least-mean-square (IF-LMS) algorithm to exploit hidden sparsity in unknown systems. Recently, the feature least-mean-square (F-LMS) algorithm has been introduced, but its application is limited to particular systems since it uses predetermined feature matrices. However, the proposed IF-LMS algorithm utilizes the stochastic gradient descent (SGD) method to learn feature matrices; thus, it can be used in any system that the classical LMS algorithm is applicable. Hence, by employing a learnable feature matrix, the IF-LMS algorithm has a vast application area as compared to the F-LMS algorithm. Moreover, mathematically, we discuss some parameters of the IF-LMS algorithm. Simulation results, in synthetic and real-life scenarios, demonstrate that the IF-LMS algorithm has superior filtering accuracy to the well-known LMS algorithm.

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Intelligent Solar Grid Integration: Advancements in Control Strategies and Power Quality Enhancement

Shambhu Choudhary,

Nath Gupta,

Hussain

2024

Circuit Theory & Apps

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This study delves into the advancements, challenges, and opportunities in the solar grid technology, emphasizing its integration into the existing power infrastructure. The proposed ML‐FOGI (multilayer fourth‐order generalized integrator) algorithm presents a promising solution for modern grid synchronization requirements, addressing challenges posed by the integration of renewable energy into the distributed generation. This technology enhances system robustness and dynamic response, particularly in weak distribution grids characterized by voltage distortions and imbalances. This innovative approach enhances system stability and performance, particularly in polluted grid conditions. Through MATLAB/Simulink modelling and experimental validation, the effectiveness of the proposed control strategy is investigated across diverse testing conditions. Emphasizing the significant role of the control strategy in enhancing power quality and grid stability in the solar photovoltaic systems, this research underscores the importance of robust and adaptive control mechanisms for optimizing performance and ensuring grid reliability in modern microgrid. This study delves into this complex issue of working of the grid connected photovoltaic (GCPV) system, emphasizing the importance of balanced unit templates and a stable DC link voltage with unpredictable grid harmonics. This research aims to enhance the resilience of solar PV systems against grid disturbances by drawing inspiration from the adaptable nature of ADALINE filters and adhering to the IEEE519 requirements. This study employs OPAL‐RT's resilient experimental configuration.

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Sparsity-Aware Data-Selective Adaptive Filters

Cited by 85 publications

References 34 publications

Performance evaluation of adaptive filters for sparse wireless channel estimation

Performance evaluation of adaptive filters for sparse wireless channel estimation

Improved feature least mean square algorithm

Intelligent Solar Grid Integration: Advancements in Control Strategies and Power Quality Enhancement

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