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Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation

Signal Processing, ISSN 0165-1684, 11/2016, Volume 128, pp. 243 - 251

A type of norm-adaption penalized least mean square/fourth (NA-LMS/F) algorithm is proposed for sparse channel estimation applications. The proposed NA-LMS/F...

Least mean square/fourth | Adaptive filter | Sparse channel estimation | Zero attracting | Norm-adaption penalty | CONVERGENCE ANALYSIS | CONSTRAINT LMS ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | PNLMS ALGORITHM | P-NORM | SELECTION | SQUARES | SYSTEM-IDENTIFICATION | RNA | Algorithms | Computer simulation | Least mean squares | Least mean squares algorithm | Norms | Signal processing | Channels | Convergence

Least mean square/fourth | Adaptive filter | Sparse channel estimation | Zero attracting | Norm-adaption penalty | CONVERGENCE ANALYSIS | CONSTRAINT LMS ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | PNLMS ALGORITHM | P-NORM | SELECTION | SQUARES | SYSTEM-IDENTIFICATION | RNA | Algorithms | Computer simulation | Least mean squares | Least mean squares algorithm | Norms | Signal processing | Channels | Convergence

Journal Article

Mechanical Systems and Signal Processing, ISSN 0888-3270, 04/2019, Volume 120, pp. 69 - 82

•The filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control.•A convex combination of the FXLMS/F algorithm (C-FXLMS/F) for active...

Convex combination | Least mean square/fourth | Adaptive filtering | Active noise control | LMS ALGORITHM | AFFINE PROJECTION ALGORITHM | ADAPTIVE COMBINATION | CONTROL SYSTEM | ENGINEERING, MECHANICAL | VARIABLE TAP-LENGTH | Mean square values | Algorithms | Computer simulation | Noise reduction | Stability analysis | Noise control | Active control | Convergence

Convex combination | Least mean square/fourth | Adaptive filtering | Active noise control | LMS ALGORITHM | AFFINE PROJECTION ALGORITHM | ADAPTIVE COMBINATION | CONTROL SYSTEM | ENGINEERING, MECHANICAL | VARIABLE TAP-LENGTH | Mean square values | Algorithms | Computer simulation | Noise reduction | Stability analysis | Noise control | Active control | Convergence

Journal Article

PLoS ONE, ISSN 1932-6203, 05/2017, Volume 12, Issue 5, p. e0176099

This paper proposes an innovative internet of things (IoT) based communication framework for monitoring microgrid under the condition of packet dropouts in...

SMART | MATRIX | MULTIDISCIPLINARY SCIENCES | Models, Theoretical | Algorithms | Computer Simulation | Wireless Technology | Least-Squares Analysis | Internet | Renewable Energy | Engineering research | Wireless sensor networks | Design and construction | Load | Semidefinite programming | Energy use | Computer simulation | Energy sources | Electricity distribution | Communications networks | Internet of Things | Information systems | Condition monitoring | Communications systems | Embedded systems | Energy resources | Wireless networks | State space models | Distributed generation | Electric utilities | Mathematical models | Sensors | Kalman filters | Energy management | State estimation

SMART | MATRIX | MULTIDISCIPLINARY SCIENCES | Models, Theoretical | Algorithms | Computer Simulation | Wireless Technology | Least-Squares Analysis | Internet | Renewable Energy | Engineering research | Wireless sensor networks | Design and construction | Load | Semidefinite programming | Energy use | Computer simulation | Energy sources | Electricity distribution | Communications networks | Internet of Things | Information systems | Condition monitoring | Communications systems | Embedded systems | Energy resources | Wireless networks | State space models | Distributed generation | Electric utilities | Mathematical models | Sensors | Kalman filters | Energy management | State estimation

Journal Article

International Journal of Adaptive Control and Signal Processing, ISSN 0890-6327, 11/2018, Volume 32, Issue 11, pp. 1644 - 1654

Summary Two novel adaptive filtering algorithms based on the mixed square/fourth error criterion are proposed for solving sparse system identification...

sparse system identification | least mean square/fourth | proportionate | unbiasedness criterion | MEAN-SQUARE ALGORITHM | LMS ALGORITHM | LEAST | STABILIZATION | CHANNEL ESTIMATION | AUTOMATION & CONTROL SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Random noise | Adaptive systems | Filtration | Computer simulation | Misalignment | Bias | Adaptive filters | Adaptive algorithms | Performance enhancement | System identification | Criteria

sparse system identification | least mean square/fourth | proportionate | unbiasedness criterion | MEAN-SQUARE ALGORITHM | LMS ALGORITHM | LEAST | STABILIZATION | CHANNEL ESTIMATION | AUTOMATION & CONTROL SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Random noise | Adaptive systems | Filtration | Computer simulation | Misalignment | Bias | Adaptive filters | Adaptive algorithms | Performance enhancement | System identification | Criteria

Journal Article

Gaojishu Tongxin/Chinese High Technology Letters, ISSN 1002-0470, 09/2018, Volume 28, Issue 9-10, pp. 852 - 860

Journal Article

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), ISSN 2166-9570, 09/2013, pp. 296 - 300

Broadband signal transmission over frequency-selective fading channel often requires accurate channel state information at receiver. One of the most attracting...

Least squares approximations | Channel estimation | Estimation | Signal processing algorithms | Cost function | Approximation algorithms | least mean square fourth (LMS/F) | zero-attracting least mean square/fourth (ZA-LMS/F) | adaptive sparse channel estimation (ASCE) | re-weighted zero-attracting least mean square/fourth (RZA-LMS/F) | Standards | Re-weighted zero-attracting least mean square/fourth (RZA-LMS/F) | Adaptive sparse channel estimation (ASCE) | Least mean square fourth (LMS/F) | Zero-attracting least mean square/fourth (ZA-LMS/F)

Least squares approximations | Channel estimation | Estimation | Signal processing algorithms | Cost function | Approximation algorithms | least mean square fourth (LMS/F) | zero-attracting least mean square/fourth (ZA-LMS/F) | adaptive sparse channel estimation (ASCE) | re-weighted zero-attracting least mean square/fourth (RZA-LMS/F) | Standards | Re-weighted zero-attracting least mean square/fourth (RZA-LMS/F) | Adaptive sparse channel estimation (ASCE) | Least mean square fourth (LMS/F) | Zero-attracting least mean square/fourth (ZA-LMS/F)

Conference Proceeding

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, ISSN 1063-8210, 09/2017, Volume 25, Issue 9, pp. 2588 - 2601

This paper presents a framework based on the logarithmic number system to implement adaptive filters with error nonlinearities in hardware. The framework is...

Algorithm design and analysis | fixed-point arithmetic | mean square deviation (MSD) | Adaptive algorithms | Adaptive filters | Computer architecture | Very large scale integration | Cost function | least mean square/fourth (LMS/LMF) algorithm | logarithmic number system (LNS) | Steady-state | Convergence | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | IMPULSIVE NOISE | POWER | SIGNAL-PROCESSING SYSTEMS | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS ALGORITHM | ADAPTATION | COMPUTATION | TRANSFORMS

Algorithm design and analysis | fixed-point arithmetic | mean square deviation (MSD) | Adaptive algorithms | Adaptive filters | Computer architecture | Very large scale integration | Cost function | least mean square/fourth (LMS/LMF) algorithm | logarithmic number system (LNS) | Steady-state | Convergence | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | IMPULSIVE NOISE | POWER | SIGNAL-PROCESSING SYSTEMS | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS ALGORITHM | ADAPTATION | COMPUTATION | TRANSFORMS

Journal Article

Wireless Communications and Mobile Computing, ISSN 1530-8669, 08/2015, Volume 15, Issue 12, pp. 1649 - 1658

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth...

least mean square/fourth (LMS/F) | l p -norm LMS/F | l 0 -norm LMS/F | sparse penalty | least mean square | least mean fourth | adaptive system identification | MULTIPATH INTERFERENCE | l(p)-norm LMS | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | fourth (LMS | NORM | CHANNEL ESTIMATION | l-norm LMS | Algorithms | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Performance enhancement | Mathematical models | Deviation | Standards

least mean square/fourth (LMS/F) | l p -norm LMS/F | l 0 -norm LMS/F | sparse penalty | least mean square | least mean fourth | adaptive system identification | MULTIPATH INTERFERENCE | l(p)-norm LMS | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | fourth (LMS | NORM | CHANNEL ESTIMATION | l-norm LMS | Algorithms | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Performance enhancement | Mathematical models | Deviation | Standards

Journal Article

Entropy, ISSN 1099-4300, 06/2017, Volume 19, Issue 6, p. 281

In this paper, a sparse set-membership proportionate normalized least mean square (SM-PNLMS) algorithm integrated with a correntropy induced metric (CIM)...

Sparse adaptive filtering | Correntropy induced metric (CIM) | Zero attracting algorithm | PNLMS algorithm | Set-membership proportionate normalized least mean square | zero attracting algorithm | PHYSICS, MULTIDISCIPLINARY | SPARSE SYSTEM-IDENTIFICATION | STEP-SIZE | FILTERS | set-membership proportionate normalized least mean square | LMS ALGORITHM | MEAN SQUARE/FOURTH ALGORITHM | AFFINE PROJECTION ALGORITHM | NORM | sparse adaptive filtering | correntropy induced metric (CIM) | NLMS ALGORITHMS | CRITERION | SQUARES

Sparse adaptive filtering | Correntropy induced metric (CIM) | Zero attracting algorithm | PNLMS algorithm | Set-membership proportionate normalized least mean square | zero attracting algorithm | PHYSICS, MULTIDISCIPLINARY | SPARSE SYSTEM-IDENTIFICATION | STEP-SIZE | FILTERS | set-membership proportionate normalized least mean square | LMS ALGORITHM | MEAN SQUARE/FOURTH ALGORITHM | AFFINE PROJECTION ALGORITHM | NORM | sparse adaptive filtering | correntropy induced metric (CIM) | NLMS ALGORITHMS | CRITERION | SQUARES

Journal Article

2014 IEEE/CIC International Conference on Communications in China (ICCC), ISSN 2377-8644, 10/2014, pp. 370 - 374

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse...

Wireless communication | reweighted ℓ 1 -norm sparse penalty | Least squares approximations | Channel estimation | Signal processing algorithms | Adaptive sparse channel estimation | Bandwidth | Vectors | zero-attracting least mean square/fourth (ZA-LMS/F) | Broadband communication | compressive sensing | Reweighted ℓ | Compressive sensing | Zero-attracting least mean square/fourth (ZA-LMS/F) | norm sparse penalty

Wireless communication | reweighted ℓ 1 -norm sparse penalty | Least squares approximations | Channel estimation | Signal processing algorithms | Adaptive sparse channel estimation | Bandwidth | Vectors | zero-attracting least mean square/fourth (ZA-LMS/F) | Broadband communication | compressive sensing | Reweighted ℓ | Compressive sensing | Zero-attracting least mean square/fourth (ZA-LMS/F) | norm sparse penalty

Conference Proceeding

International Journal of Communication Systems, ISSN 1074-5351, 05/2017, Volume 30, Issue 8, pp. np - n/a

Summary Sparse least‐mean mixed‐norm (LMMN) algorithms are developed to improve the estimation performance for sparse channel estimation applications. Both the...

sparse channel estimation | zero attracting | LMS | adaptive filter | LMF | least‐mean mixed‐norm | least-mean mixed-norm | CRITERION | TELECOMMUNICATIONS | SQUARES | SYSTEM-IDENTIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Broadband | Least mean squares | Mathematical analysis | Adaptive filters | Least mean squares algorithm | Channels | Convergence

sparse channel estimation | zero attracting | LMS | adaptive filter | LMF | least‐mean mixed‐norm | least-mean mixed-norm | CRITERION | TELECOMMUNICATIONS | SQUARES | SYSTEM-IDENTIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Broadband | Least mean squares | Mathematical analysis | Adaptive filters | Least mean squares algorithm | Channels | Convergence

Journal Article

Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers, ISSN 1058-6393, 1996, pp. 1191 - 1194 vol.2

This article presents a novel algorithm for echo cancellers with near-end and far-end sections. The algorithm consists of simultaneously applying the least...

Additive noise | Echo cancellers | Computational modeling | Adaptive filters | Cost function | Noise cancellation | System identification | Least squares approximation | Physics | Convergence

Additive noise | Echo cancellers | Computational modeling | Adaptive filters | Cost function | Noise cancellation | System identification | Least squares approximation | Physics | Convergence

Conference Proceeding

Entropy, ISSN 1099-4300, 2016, Volume 18, Issue 10, pp. 380 - 380

A robust sparse least-mean mixture-norm (LMMN) algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path...

Broadband multi-path sparse channel estimation | Least mean fourth | Least-mean mixed-norm | LMS | Correntropy-induced metric | Adaptive filters | adaptive filters | PHYSICS, MULTIDISCIPLINARY | broadband multi-path sparse channel estimation | least-mean mixed-norm | least mean fourth | correntropy-induced metric | Algorithms | Broadband | Misalignment | Least mean squares | Mathematical analysis | Estimating | Channels

Broadband multi-path sparse channel estimation | Least mean fourth | Least-mean mixed-norm | LMS | Correntropy-induced metric | Adaptive filters | adaptive filters | PHYSICS, MULTIDISCIPLINARY | broadband multi-path sparse channel estimation | least-mean mixed-norm | least mean fourth | correntropy-induced metric | Algorithms | Broadband | Misalignment | Least mean squares | Mathematical analysis | Estimating | Channels

Journal Article

EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, 12/2013, Volume 2013, Issue 1, pp. 1 - 18

Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To...

Engineering | Compressive sensing | Signal, Image and Speech Processing | Normalized LMS | Adaptive sparse channel estimation | ℓ p -Norm normalized least mean square | ℓ 0 -Norm normalized least mean square | Least mean square | ℓ0-Norm normalized least mean square | ℓp-Norm normalized least mean square | LMS | l Norm normalized least mean square | WIRELESS | NORM | TELECOMMUNICATIONS | l(p)-Norm normalized least mean square | SYSTEM-IDENTIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Frequency estimation | Methods | Least squares

Engineering | Compressive sensing | Signal, Image and Speech Processing | Normalized LMS | Adaptive sparse channel estimation | ℓ p -Norm normalized least mean square | ℓ 0 -Norm normalized least mean square | Least mean square | ℓ0-Norm normalized least mean square | ℓp-Norm normalized least mean square | LMS | l Norm normalized least mean square | WIRELESS | NORM | TELECOMMUNICATIONS | l(p)-Norm normalized least mean square | SYSTEM-IDENTIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Frequency estimation | Methods | Least squares

Journal Article

2016 24th European Signal Processing Conference (EUSIPCO), ISSN 2219-5491, 08/2016, Volume 2016-, pp. 2380 - 2384

A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The proposed algorithm is...

LMS | Signal processing algorithms | Channel estimation | Estimation | Europe | sparse channel estimation | Cost function | sparse adaptive filtering | LMS/F | Steady-state | least-mean mixed-norm | Convergence | Sparse adaptive filtering | Least-mean mixed-norm | Sparse channel estimation

LMS | Signal processing algorithms | Channel estimation | Estimation | Europe | sparse channel estimation | Cost function | sparse adaptive filtering | LMS/F | Steady-state | least-mean mixed-norm | Convergence | Sparse adaptive filtering | Least-mean mixed-norm | Sparse channel estimation

Conference Proceeding

Scientific World Journal, ISSN 2356-6140, 2014, Volume 2014, pp. 274897 - 10

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems...

MULTIDISCIPLINARY SCIENCES | TIME | Least-Squares Analysis | Wireless Technology | Algorithms | Usage | Models | MIMO communications | Accuracy | Fourier transforms | Mean square errors | Estimating techniques | Methods

MULTIDISCIPLINARY SCIENCES | TIME | Least-Squares Analysis | Wireless Technology | Algorithms | Usage | Models | MIMO communications | Accuracy | Fourier transforms | Mean square errors | Estimating techniques | Methods

Journal Article

Circuits, Systems, and Signal Processing, ISSN 0278-081X, 09/2017, Volume 36, Issue 9, pp. 3864 - 3873

This paper proposes a bias-compensated normalized least-mean fourth (NLMF) algorithm to compensate for the bias caused by noisy input. In the proposed...

Normalized least-mean fourth (NLMF) | System identification | Noisy input | AFFINE PROJECTION ALGORITHM | STABILITY | NLMS ALGORITHM | CONSTRAINT | SUBBAND ADAPTIVE FILTER | ENGINEERING, ELECTRICAL & ELECTRONIC | Electrical engineering | Algorithms | Computer simulation | Misalignment | Compensation | Bias

Normalized least-mean fourth (NLMF) | System identification | Noisy input | AFFINE PROJECTION ALGORITHM | STABILITY | NLMS ALGORITHM | CONSTRAINT | SUBBAND ADAPTIVE FILTER | ENGINEERING, ELECTRICAL & ELECTRONIC | Electrical engineering | Algorithms | Computer simulation | Misalignment | Compensation | Bias

Journal Article

Signal, Image and Video Processing, ISSN 1863-1703, 3/2016, Volume 10, Issue 3, pp. 503 - 510

The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean...

Engineering | Signal, Image and Speech Processing | Image Processing and Computer Vision | Computer Imaging, Vision, Pattern Recognition and Graphics | Impulsive noise | Reweighted zero-attracting | Multimedia Information Systems | Correntropy induced metric | Zero-attracting | Sparse channel estimation | Least mean p-power | EQUALIZATION | GAUSSIAN STABLE PROCESSES | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS | ADAPTIVE FILTERING ALGORITHMS | CORRENTROPY | SYSTEM-IDENTIFICATION

Engineering | Signal, Image and Speech Processing | Image Processing and Computer Vision | Computer Imaging, Vision, Pattern Recognition and Graphics | Impulsive noise | Reweighted zero-attracting | Multimedia Information Systems | Correntropy induced metric | Zero-attracting | Sparse channel estimation | Least mean p-power | EQUALIZATION | GAUSSIAN STABLE PROCESSES | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS | ADAPTIVE FILTERING ALGORITHMS | CORRENTROPY | SYSTEM-IDENTIFICATION

Journal Article

International Journal of Communication Systems, ISSN 1074-5351, 01/2015, Volume 28, Issue 1, pp. 38 - 48

SUMMARY Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often...

adaptive system identifications (ASI) | least mean square (LMS) | least mean fourth (LMF) | sparse penalty | adaptive sparse system identifications (ASSI) | Least mean square (LMS) | Sparse penalty | Adaptive system identifications (ASI) | Least mean fourth (LMF) | Adaptive sparse system identifications (ASSI) | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Analysis | Algorithms | Adaptive systems | Stability | Equivalence | Computer simulation | Least mean squares | Performance enhancement | System identification

adaptive system identifications (ASI) | least mean square (LMS) | least mean fourth (LMF) | sparse penalty | adaptive sparse system identifications (ASSI) | Least mean square (LMS) | Sparse penalty | Adaptive system identifications (ASI) | Least mean fourth (LMF) | Adaptive sparse system identifications (ASSI) | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Analysis | Algorithms | Adaptive systems | Stability | Equivalence | Computer simulation | Least mean squares | Performance enhancement | System identification

Journal Article

IET Communications, ISSN 1751-8628, 11/2015, Volume 9, Issue 17, pp. 2168 - 2175

Large-scale multiple-input multiple-output (MIMO) system is considered one of promising technologies to realise next-generation wireless communication system...

Research Articles | least mean squares methods | adaptive filters | 5G mobile communication | UPLINK | next generation networks | low-complexity large-scale MIMO channel estimation method | low-complexity large-scale multiple input multiple output channel estimation | sparse adaptive least mean square filter affine combination | next-generation wireless communication system | stochastic gradient search method | ENGINEERING, ELECTRICAL & ELECTRONIC | cluster-sparse structure | LMS filter | channel estimation | pattern clustering | FREQUENCY TRAINING OFDM | MIMO | wireless channels | gradient methods | MIMO communication | search problems | computational complexity | MIMO (control systems) | Algorithms | Search methods | Least mean squares | Adaptive filters | Least mean squares algorithm | Mathematical models | Channels

Research Articles | least mean squares methods | adaptive filters | 5G mobile communication | UPLINK | next generation networks | low-complexity large-scale MIMO channel estimation method | low-complexity large-scale multiple input multiple output channel estimation | sparse adaptive least mean square filter affine combination | next-generation wireless communication system | stochastic gradient search method | ENGINEERING, ELECTRICAL & ELECTRONIC | cluster-sparse structure | LMS filter | channel estimation | pattern clustering | FREQUENCY TRAINING OFDM | MIMO | wireless channels | gradient methods | MIMO communication | search problems | computational complexity | MIMO (control systems) | Algorithms | Search methods | Least mean squares | Adaptive filters | Least mean squares algorithm | Mathematical models | Channels

Journal Article

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