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Comparative Studies of LMS and NLMS Algorithm for Adaptive Beam Forming System

Authors(4):

Sonia Hokam, Kirti Mundale, Khushbu Jambulkar, Yogita Khandar
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Smart antennas have been considered to be one of the most demanding communication technologies. It adapted as most demanding technology because of high-bit rate or high quality in broadband commercial wireless communication. Then most elementary solution would be to increase bandwidth; however, this becomes ever more challenging as the electromagnetic spectrum is becoming increasingly congested. Smart antenna systems provide opportunities for higher system capacity and improved quality of service. Direction-of-arrival (DOA) estimation is based on the LMS and NLMS algorithm for identifying the directions of the source signals incident on the sensor array comprising the smart antenna system. . This is done through smart-antenna arrays and the associated adaptive beamforming algorithms. Adaptive beam forming is achieved using this algorithm for directing the main beam towards the desired source signals and generating deep nulls in the directions of interfering signals. It has been found that NLMS performs better in many respects than LMS and so we propose NLMS to be used by mobile companies when they will use smart antenna. Our comparison and findings were simulated using MATLAB.

Sonia Hokam, Kirti Mundale, Khushbu Jambulkar, Yogita Khandar

Beamforming, DOA, LMS, NLMS algorithm.

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Publication Details

Published in : Volume 2 | Issue 1 | January-Febuary - 2016
Date of Publication Print ISSN Online ISSN
2016-02-08 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
290-292 IJSRSET162167   Technoscience Academy

Cite This Article

Sonia Hokam, Kirti Mundale, Khushbu Jambulkar, Yogita Khandar, "Comparative Studies of LMS and NLMS Algorithm for Adaptive Beam Forming System", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.290-292, January-Febuary-2016.
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