Modified Filtered-X Hierarchical LMS Algorithm with Sequential Partial Updates for Active Noise Control

Lorente, Pedro Ramos; Ferrer, Raúl Martín; Martínez, Fernando Arranz; Palacios-Navarro, Guillermo

doi:10.3390/app11010344

Cited by 5 publications

(3 citation statements)

References 31 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Partial Update (PU) algorithms have gained significant attention in recent years, both in terms of research and practical applications [11][12][13][14][15][16][17][18][19][20][21][22][23][24], but they have yet to be applied to array beamforming systems. According to our knowledge, no previous studies have used adaptive partial update (PU) methods for beamforming array antennas.…”

Section: Introductionmentioning

confidence: 99%

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

A. Shubber,

M. Jamel,

K. Nahar

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

The most important issues for improving the performance of modern wireless communication systems are interference cancellation, efficient use of energy, improved spectral efficiency and increased system security. Beamforming Array Antenna (BAA) is one of the efficient methods used for this purpose. Full band BAA, on the other hand, will suffer from a large number of controllable elements, a long convergence time and the complexity of the beamforming network. Since no attempt had previously been made to use Partial Update (PU) for BAA, the main novelty and contribution of this paper was to use PU instead of full band adaptive algorithms. PU algorithms will connect to a subset of the array elements rather than all of them. As a result, a common number of working antennas for the system's entire cells can be reduced to achieve overall energy efficiency and high cost-effectiveness. In this paper, we propose a new architectural model that employs PU adaptive algorithms to control and minimize the number of phase shifters, thereby reducing the number of base station antennas. We will concentrate on PU LMS (Least Mean Square) algorithms such as sequential-LMS, M-max LMS, periodic-LMS, and stochastic-LMS. According to simulation results using a Uniform Linear Array (ULA) and three communications channels, the M-max-LMS, periodic LMS, and stochastic LMS algorithms perform similarly to the full band LMS algorithm in terms of square error, tracking weight coefficients, and estimation input signal, with a quick convergence time, low level of error signal at steady state and keeping null steering's interference-suppression capability intact.

show abstract

Section: Introductionmentioning

confidence: 99%

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

A. Shubber,

M. Jamel,

K. Nahar

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…1 Active noise control (ANC) is one of the most effective methods to control noise. 3 However, the LMS algorithm used by the traditional ANC system has a contradiction in terms of convergence speed and steady-state error, that is, to make the algorithm converge quickly, the result of steady-state error is very large, so the LMS algorithm must make a compromise. [4][5][6] With the upgrading of hardware equipment, the calculation speed of the optional ANC controller is faster and faster, and the complexity of the computable algorithm is also higher and higher, which provides the possibility for the application of the more complex variable step size LMS algorithm in practice.…”

Section: Introductionmentioning

confidence: 99%

Performance optimization of the active noise control algorithm based on logarithmic function and its application in secondary channel online identification ANC system

Zhang

Wang

Yang

et al. 2022

Measurement and Control

View full text Add to dashboard Cite

In this paper, several methods about the active noise control (ANC) system are studied. The common adaptive recognition system based on the variable step filter x least mean square (FxLMS) algorithm are elaborated. By introducing the momentum term into the variable step algorithm, the value range of the variable step is increased, so as to speed up the convergence of the algorithm and improve the effect of noise reduction. The weight of the control filter based on the improved logarithmic function variable step size least mean square (VSSLMS) algorithm is updated in the improved second channel online recognition algorithm. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results show that the improved algorithm structure could improve the convergence speed and reduce the noises of the whole system at three different primary noises. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results under three different primary noises show that the improved algorithm structure could improve the convergence speed and reduce the noise of the whole system.

show abstract

“…In this sense, gain in step-size [29] is a parameter defined in the context of periodic primary noise attenuation when PU of the weights of the filter are used to lower the computational cost of an adaptive algorithm [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Approach to Obtain a New Gain in Step-Size of LMS Filters Dealing with Periodic Signals

et al. 2021

Self Cite

View full text Add to dashboard Cite

Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved as a valid strategy in the active control of periodic noise consisting of a sum of harmonics. The convergence analysis carried out here is based on the periodic nature of the input signal, which makes it possible to formulate the adaptive process with a matrix-based approach, the periodic least-mean-square (P-LMS) algorithm In this paper, we obtain the upper bound that limits the step-size parameter of the sequential PU P-LMS algorithm and compare it to the bound of the full-update P-LMS algorithm. Thus, the limiting value for the step-size parameter is expressed in terms of the step-size gain of the PU algorithm. This gain in step-size is the quotient between the upper bounds ensuring convergence in the following two scenarios: first, when PU are carried out and, second, when every coefficient is updated during every cycle. This step-size gain gives the factor by which the step-size can be multiplied so as to compensate for the convergence speed reduction of the sequential PU algorithm, which is an inherently slower strategy. Results are compared with previous results based on the standard sequential PU LMS formulation. Frequency-dependent notches in the step-size gain are not present with the matrix-based formulation of the P-LMS. Simulated results confirm the expected behavior.

show abstract

Modified Filtered-X Hierarchical LMS Algorithm with Sequential Partial Updates for Active Noise Control

Cited by 5 publications

References 31 publications

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

Performance optimization of the active noise control algorithm based on logarithmic function and its application in secondary channel online identification ANC system

An Alternative Approach to Obtain a New Gain in Step-Size of LMS Filters Dealing with Periodic Signals

Contact Info

Product

Resources

About