On the LMS Algorithm Performance for Interference Elimination in Smart Antennas Array
Wilander Testone Pereira da Silva1, João Viana da Fonseca Neto2

1BSc Wilander Testone P. da Silva, develops research in the Embedded Systems and Intelligent Control Laboratory (Lab.SECI) that is associated to the Electrical Engineering Graduate Program (PPGEE) of the Federal University of Maranhão (UFMA), São Luís, Brazil.
2Dr. João Viana da F. Neto, professor of Electrical Engineering Department of Federal University of Maranhão and Electrical Engineering Graduate Program (PPGEE). He is the header of the Embedded Systems and Intelligent Control Laboratory (Lab.SECI) of the Federal University of Maranhão (UFMA), São Luís, Brazil, (e-mail: São Luís, Brazil.

Manuscript received on 15 December 2015 | Revised Manuscript received on 25 December 2015 | Manuscript Published on 30 December 2015 | PP: 73-78 | Volume-5 Issue-2, December 2015 | Retrieval Number: B4342125215/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Theefficient use of limited radio frequency spectrum is possible due tothe smart antenna arrays.These antennas arrays incorporateadaptive algorithms, such as: Least Mean Square (LMS) algorithm, whichfinds the spatial temporal filtergains or weights according to the signal environment behavior. In terms of the mean error and mean squared error convergences of the LMS algorithm, the performance evaluationofthealgorithm is oriented by its convergence properties and the improvements in the mobile communication systems.In this paper is presented the LMS algorithm to solve the beamforming problem and antenna array concepts, as well as, it is presented general performance analysis,in terms of the LMSbeamformer to eliminate interference in antennas array.The potentialities of adaptive design are verified in modelsof smart linear antenna arrays. These antenna arrays models are connected to the beamformer model. The integration of these models allows todesign the adaptive beamformer. The results obtained from simulations of themodels showsthat the LMS algorithm is a good alternative for smart antenna design in mobile communication environment, due to the directivity improvement promoted in the antenna array.
Keywords: Smart Antenna Array, Adaptive Filter, LMS Algorithm, Algorithm Convergence, Beamforming, Interferenceelimination, Mobile Communication, Wireless Communications.

Scope of the Article: Smart Antenna