A Pipelined Reduced Complexity Two-Stages Parallel LMS Structure for Adaptive Beamforming

Akkad, Ghattas; Mansour, Ali; El-Hassan, Bachar; Inaty, Elie; Ayoubi, Rafic; Srar, Jalal Abdulsayed

doi:10.1109/tcsi.2020.2994812

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…Another double-stage LLMS variant [ 20 ] was formulated for concentric circular arrays. In order to address the concerns about complexity and to facilitate practical hardware implementation a Parallel LMS algorithm [ 21 ] was introduced. A scheme is still required that maintains a balance among convergence speed, mean square error (MSE), and computational cost.…”

Section: Introductionmentioning

confidence: 99%

Self correction fractional least mean square algorithm for application in digital beamforming

Shah,

Jan,

Shah

et al. 2024

PLoS ONE

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Fractional order algorithms demonstrate superior efficacy in signal processing while retaining the same level of implementation simplicity as traditional algorithms. The self-adjusting dual-stage fractional order least mean square algorithm, denoted as LFLMS, is developed to expedite convergence, improve precision, and incurring only a slight increase in computational complexity. The initial segment employs the least mean square (LMS), succeeded by the fractional LMS (FLMS) approach in the subsequent stage. The latter multiplies the LMS output, with a replica of the steering vector (Ŕ) of the intended signal. Mathematical convergence analysis and the mathematical derivation of the proposed approach are provided. Its weight adjustment integrates the conventional integer ordered gradient with a fractional-ordered. Its effectiveness is gauged through the minimization of mean square error (MSE), and thorough comparisons with alternative methods are conducted across various parameters in simulations. Simulation results underscore the superior performance of LFLMS. Notably, the convergence rate of LFLMS surpasses that of LMS by 59%, accompanied by a 49% improvement in MSE relative to LMS. So it is concluded that the LFLMS approach is a suitable choice for next generation wireless networks, including Internet of Things, 6G, radars and satellite communication.

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Section: Introductionmentioning

confidence: 99%

Self correction fractional least mean square algorithm for application in digital beamforming

Shah,

Jan,

Shah

et al. 2024

PLoS ONE

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“…The unprecedented increase in wireless connected devices has tightened the constraints on popular adaptive algorithms, such as: Least Mean Square (LMS), Recursive Least Square (RLS) and various variants [1][2][3][4][5][6][7][8][9][10], when targeting beamforming applications [11][12][13]. Such constraints are reflected by the requirement of an accelerated convergence rate, a high precision beam pointing accuracy and a reduced complexity structure suitable for a hardware implementation [14][15][16][17]. Recently, two multi-stage adaptive beamforming algorithms have been proposed to eliminate the tradeoff between the LMS convergence speed and its steady state error [18,19]: parallel LMS (pLMS) [18], and the parallel RLMS (RLMSp) [19].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the pLMS with a linear computational complexity of order O(N ), where N represents the number of antenna elements, the RLMSp presents an undesirable quadratic complexity of order O(N 2 ) and requires the use of a division operation [17]. However simple and effective, the pLMS does not abide to the pre-set constraints, it doubles the resource requirements of the classical LMS and requires computing an independent set of weights, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, and in order to simplify the pLMS structure, a reduced complexity parallel LMS (RC-pLMS) [17] has been proposed. The RC-pLMS is built from a single LMS stage with modified inputs formed as a linear combination of the past and present value [17], thus reducing the complexity of the pLMS by half. Therefore, it is of great interest to further evaluate its behavior and stability for different step sizes.…”

Section: Introductionmentioning

confidence: 99%

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Stability Analysis of the RC-PLMS Adaptive Beamformer Using a Simple Transfer Function Approximation

Akkad

Mansour

Hassan

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a discrete time transfer function approximation for the reduced complexity parallel least mean square (RC-pLMS) adaptive beamforming algorithm. The RC-pLMS is built using a single least mean square (LMS) stage whose inputs are obtained as a linear combination of the present and past sample. Thus, in order to numerically assess the RC-pLMS stability and to determine the approximate maximum parametric value of the step size for which it remains stable, we derive its discrete time transfer function approximate. In this approximation, the input uniform linear antenna array is remodeled as a finite impulse response (FIR) fractional delay Farrow filter. Computer simulations, presented by the mean square error and beam radiation pattern, demonstrates the validity of the transfer function approximate. Additionally, the RC-pLMS stability is evaluated, with respect to the pole-zero plot, for different step sizes and the approximate upper bound value of the step size is determined.

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A Self Referencing Technique for the RC-pLMS Adaptive Beamformer and Its Hardware Implementation

Akkad

Mansour

El-Hassan

et al. 2022

Lecture Notes in Electrical Engineering

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A Pipelined Reduced Complexity Two-Stages Parallel LMS Structure for Adaptive Beamforming

Cited by 14 publications

References 29 publications

Self correction fractional least mean square algorithm for application in digital beamforming

Self correction fractional least mean square algorithm for application in digital beamforming

Stability Analysis of the RC-PLMS Adaptive Beamformer Using a Simple Transfer Function Approximation

A Self Referencing Technique for the RC-pLMS Adaptive Beamformer and Its Hardware Implementation

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