Performance Analysis of LMS and NLMS Algorithms for a Smart Antenna System

Yasin, Muhammad; Akhtar, Pervez; Valiuddin,

doi:10.5120/854-1196

Cited by 10 publications

(9 citation statements)

References 5 publications

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“…Since SMI employs direct matrix inversion the convergence of this algorithm is much faster compared to the LMS algorithm [12].…”

Section: B Smi Algorithmsmentioning

confidence: 99%

“…by the equation ( 10), j sin(ωt ) s (t) =e (12) The interfering signals u i (t) arriving at angles θ i is also of the same form. By doing so it can be shown in the simulations how interfering signals of the same frequency as the desired signal can be separated to achieve rejection of co-channel interference.…”

Section: Issn No 2582-0958 __________________________________________...mentioning

confidence: 99%

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Performance Comparison of Beam Forming Technique Using LMS and Smi Algorithms

Rajiv¹,

Lakshmi²,

Divya³

2021

IJTRET

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The main objective is to analyse adaptive beam forming approach based on smart antenna. Several algorithms have been developed based on different criteria to compute the complex weights. In this paper the comparison of the performance of algorithms namely Least Mean Square algorithm (LMS) and Sample Matrix Inversion algorithm (SMI) are presented. The main advantage of these algorithms is its simplicity with a minimal loss of accuracy. This paper describes the design of an adaptive antenna array after receiving the signals from the desired and interfering directions. Then the weight vector is evaluated to minimize the error that provides an appropriate beam pattern to each subscriber. For these two algorithms the mean square error and Array factor are evaluated and compared. Simulation results revealed that Least Mean Squares (LMS) are best for beam forming (to form main lobes) towards desired user but they have limitations towards interference rejection. While Sample Matrix Inversion Algorithm (SMI) has satisfactory response towards beam forming and it gives better outcome for interference rejection. It is verified that convergence rate of SMI is faster than LMS so SMI is proved the best choice. The effect of changing number of elements on Array factor for SMI algorithm has also been studied.

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“…Since SMI employs direct matrix inversion the convergence of this algorithm is much faster compared to the LMS algorithm [12].…”

Section: B Smi Algorithmsmentioning

confidence: 99%

Section: Issn No 2582-0958 __________________________________________...mentioning

confidence: 99%

Performance Comparison of Beam Forming Technique Using LMS and Smi Algorithms

Rajiv¹,

Lakshmi²,

Divya³

2021

IJTRET

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show abstract

“…1. These ABF algorithms 13–15 are used to update the weights dynamically so that the mean‐square error (MSE) is reduced and the SNR of the desired signal is optimized.…”

Section: System Modelmentioning

confidence: 99%

Mathematical model of bessel beamformer with automatic gain control for smart antenna array system in Rayleigh fading channel

Yasin

Akhtar

Pathan³

2014

IEEJ Trans Elec Electron Eng

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This paper analyzes the mathematical model of a Bessel beamformer with automatic gain control (AGC) in a multipath scenario using a digital modulation technique and makes comparison with the one without AGC. The Bessel beamformer with AGC is designated here as the modified Bessel beamformer. The desired useful and interfering signals operate with the same carrier frequency and Doppler shifts but in different directions. Based on simulation results, this modified Bessel beamformer is shown to provide an optimum solution and to accommodate more users in real‐time base stations of mobile communication system when implemented on smart antenna array systems. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…LMS and its variants are used extensively for filter design and antenna beamforming [9][10][11][12] of adaptive antennas. But for beamforming of adaptive smart antennas either accuracy of desired beam direction or convergence of the algorithms is explained.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

Senapati

Ghatak

Roy

2015

2015 International Conference on Computational Intelligence and Networks

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This paper presents a comparative study of beamforming techniques using least mean square (LMS) algorithm and its variants, like, normalized least mean square (NLMS) algorithm and sign least mean square (SLMS) algorithm. The accuracy of beam generation toward the direction of arrival (DoA) and null generation toward the interferer, depends on the value of step size parameter used in the algorithm. Beamwidth and side lobe levels (SLL) are also compared for these three algorithms.

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Performance Analysis of LMS and NLMS Algorithms for a Smart Antenna System

Cited by 10 publications

References 5 publications

Performance Comparison of Beam Forming Technique Using LMS and Smi Algorithms

Performance Comparison of Beam Forming Technique Using LMS and Smi Algorithms

Mathematical model of bessel beamformer with automatic gain control for smart antenna array system in Rayleigh fading channel

A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

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