Adaptive beamforming using variable step-size LMS algorithm with novel ULA array configuration

Shubair, Raed M.; Hakam, Ali

doi:10.1109/icct.2013.6820455

Cited by 20 publications

(16 citation statements)

References 11 publications

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“…[17][18][19] LMS, RLS, and SMI are widely tested adaptive algorithms in various optimization processes in the wireless communication domain. 3,13,14,[18][19][20][21][22][23] In a generalized manner, all these three adaptive algorithms follow the weight adjustment technique, as shown in Figure 1.…”

Section: Adaptive Algorithms For Smart Antenna Arraymentioning

confidence: 99%

“…of iterations. 3,[13][14][15]18,20 The performance of the algorithm depends on the step size parameter (μ), which affects the convergence speed and the variation of the learning curve. The rate of convergence is slow for a small value of 'μ' but gives a good estimation of the gradient vector since large amounts of data are taken into account.…”

Section: Principle Of Lms Algorithmmentioning

confidence: 99%

“…Well-known adaptive algorithms least mean square (LMS), recursive least squares (RLS), 6,7 sample matrix inversion (SMI) used in wireless communication for signal optimization are widely used for beam shaping. [13][14][15][16][17][18][19][20][21] Our aim in this article is to design a smart antenna test-bed for antenna beam shaping in small scale fading channel with the help of wireless open-access research platform (WARP) boards, a moderately expensive, portable FPGA board, writing generic programming codes over a user-friendly MATLAB interface using the basic adaptive algorithms at first and to make comparative performance analysis. As an additional contribution, we have used a modified SMI algorithm to get the best performance in all conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Beam shaping can be visualized as an adaptive optimization of received power pattern. Well‐known adaptive algorithms least mean square (LMS), recursive least squares (RLS), 6,7 sample matrix inversion (SMI) used in wireless communication for signal optimization are widely used for beam shaping 13‐21 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Imtiaj

Bhattacharya

Misra

2020

Int J RF Microw Comput Aided Eng

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Adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distortion in the receiving signal due to multipath, small scale, and large scale fading effects. This article deals with the development of reconfigurable field programmable gate array (FPGA)-based hardware for smart antenna system to explore parameter dependencies, drawbacks, and relative performance comparison of popular adaptive beamforming and interference suppression algorithms. These are least mean square, recursive least squares (RLS), and sample matrix inversion (SMI) used in real-time under laboratory environment where the existing wireless channel between transmitters and receivers is linear timevarying in nature due to presence of secondary sources giving rise to smallscale fading. For this at first, we propose a novel received signal strength indicator-based procedure to measure the radiation pattern of the antenna under an echoic indoor environment on a reconfigurable and portable FPGA system named wireless open-access research platform (WARP), controllable by generic programming codes over a user-friendly MATLAB interface. For better performance, the SMI algorithm was modified to increase block size rather than block shifting in general SMI. Later a comparative study was performed under varying conditions to observe the best utilization of three adaptive algorithms in beam shaping. In all cases, SMI performs the best with less beam shaping error and faster convergence, validating its use in a real-time fading environment.

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Section: Adaptive Algorithms For Smart Antenna Arraymentioning

confidence: 99%

Section: Principle Of Lms Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Imtiaj

Bhattacharya

Misra

2020

Int J RF Microw Comput Aided Eng

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“…The simplest and most widely used adaptive algorithm is the Least Mean Square (LMS) algorithm and its modifications [1][2][3][4][5]. In [2], the authors used an array image factor, sandwiched in between two LMS algorithms sections.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Array Beamforming Using a Chaotic Beamforming Algorithm

Jovanović

Lazović

Rubežić

2016

International Journal of Antennas and Propagation

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The Chaotic beamforming adaptive algorithm is new adaptive method for antenna array’s radiation pattern synthesis. This adaptive method based on the optimization of the Least Mean Square algorithm using Chaos theory enables fast adaptation of antenna array radiation pattern, reduction of the noisy reference signal’s impact, and the improvement of the tracking capabilities. We performed simulations for linear and circular antenna arrays. We also compared the performances of the used and existing algorithms in terms of the radiation pattern comparison.

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HW-CMA and HW-LS-CMA Blind Learning Method for Intelligent Antenna System

Pandhare

Zalawadia

2021

Advances in Intelligent Systems and Computing

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The trend in the conception of future satellite system and modern communication systems are undoubtedly in the direction of the use of digital beamformer. These systems will include many digital links. These beamformer needs to control their weights adaptively for build intelligent systems. For controlling these weights, various blind and nonblind algorithm apply as per applications. Blind learning process is based on constant modulus algorithm (CMA) and least square CMA (LS-CMA) analyses. Also, to improve converge area and side lob level reduction, modified Hamming window-based algorithm to constant modulus algorithm and least square simulate using MATLAB. These algorithms are validated for linear, circular and planar antenna array geometry.

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Adaptive beamforming using variable step-size LMS algorithm with novel ULA array configuration

Cited by 20 publications

References 11 publications

Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Adaptive Array Beamforming Using a Chaotic Beamforming Algorithm

HW-CMA and HW-LS-CMA Blind Learning Method for Intelligent Antenna System

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