The adaptive algorithms of the smart antenna system in future mobile telecommunication systems

Zhang, Jianwu

doi:10.1109/iwat.2005.1461088

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Adaptive beam forming can be performed in many ways using adaptive algorithms. Most of the algorithms are concerned with the maximization of the SNR [7]. Adaptive array systems can locate and track signals (users and interferers) and dynamically adjust the antenna pattern to enhance reception, while minimizing interference using signal processing algorithms.…”

Section: Adaptive Beam Formingmentioning

confidence: 99%

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A Novel Adaptive Beam forming RLMS Algorithm for Smart Antenna System

Kamaraju¹,

Ramakrishna²,

Ramanjaneyulu³

2014

IJCA

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The smart antenna is a technology and has been applied to the cellular and mobile communication system. It is a combination of multiple antenna elements with a signal processing capability to optimize its radiation and/or reception pattern automatically change in response to the signal environment. The core of smart antenna is the selection of smart algorithms in adaptive array. In this paper, RLMS based adaptive beam forming algorithm is used to improve the performance of the cellular and mobile communication system. Using adaptive beam forming algorithm, the weights/coefficients of antenna arrays are adjusted to form adaptive beam with the required characteristics to track corresponding users automatically and at the same time to minimize interference arising from other users by introducing nulls in their directions. Recursive Least Mean Square (RLMS) algorithm provides a comprehensive and detailed treatment of the signal model used for beam forming. The performance of the RLMS algorithm in the presence of Multi-path effects and multiple users is analyzed using Software simulations. In the proposed method, mean square error is reduced and Convergence is improved in compared with LMS algorithm based method.

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Section: Adaptive Beam Formingmentioning

confidence: 99%

“…The most inherent capacity of RLS is its high convergence Rate [7]. The algorithm updates the autocorrelation matrix for the next instant with the aid of the autocorrelation matrix calculated for the present instant.…”

Section: Rlms Algorithmmentioning

confidence: 99%

A Novel Adaptive Beam forming RLMS Algorithm for Smart Antenna System

Kamaraju¹,

Ramakrishna²,

Ramanjaneyulu³

2014

IJCA

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“…The gradient of cost function can be approximated by the method of steepest descent and the directions of the steepest descent with the direction of the gradient vector are opposite. The steepest descent iterative is given by following formula (8) where represents the step size. Refer to the formula (4) and 5, and the LMS solution is obtained by (9).…”

Section: Least Mean Square (Lms)mentioning

confidence: 99%

Algorithms of Adaptive Beam Forming for Smart Antenna: A Comparative Study

Jethva¹

2013

IOSR-JECE

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For enhancing data rate Smart Antennas have been gaining prominence in the recent times where the main beam is steered towards direction of interest via phase shifter. The sense behind this development is the availability of high-end processors to handle the complex computations involved. The phase shifting and array weighing can be performed on digital data rather than in hardware is the major advantage of digital beam former. This paper gives comparative study of five adaptive algorithms-Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Sample Matrix Inversion (SMI), Recursive Least Square (RLS) and Conjugate Gradient Method (CGM) for computing the array weights. This adaptive algorithm assures very high rate of convergence & highly reduced mean square error. The weights obtained by the above algorithms are then used to steer the antenna array beam in the direction of interest, thereby enhancing SNR.

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