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Adaptive Smart Antenna using Neural Network (SMI Algorithm)
Authors(5) :-Bhagyashri B. Hedau, Rupali S. Pardhi,Sanjeevani A. Hiradkar, Dhanshri S. Borkar, Prof. Sonia V. Hokam
Smart antenna systems are of great importance in wireless communications and RADAR applications ,They effectively enhance the system capacity and reduce the co-channel interference. Smart antenna is an array antenna that uses adaptive beam forming algorithms to steer the main beam towards the desired signal direction and reject the interfering signals of the same frequency from other direction without moving the antenna. This is achieved by continuously updating the weights of each radiating element (antenna). An algorithm with low complexity, low computation cost, high speed convergence rate and better performance is usually preferred. This paper introduces a new performance investigation and comparison between five different beam forming algorithms : Least Mean Square(LMS), Normalised Least Mean Square(NLMS),Sample Matrix Inversion(SMI),Recursive Least Square(RLS) and Hybrid Least Mean Square/ Sample Matrix Inversion (LMS/SMI). In this investigations, the number of array element and the displacement among them are changed in each algorithm is optimized and demonstrated using MATLAB software package.
Bhagyashri B. Hedau, Rupali S. Pardhi,Sanjeevani A. Hiradkar, Dhanshri S. Borkar, Prof. Sonia V. Hokam
MATLAB, LMS, NLMS, RADAR, CDMA, SMI
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Published in : Volume 3 | Issue 2 | January-February 2017
Date of Publication : 2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 27-29
Manuscript Number : NCAEAS2308
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Bhagyashri B. Hedau, Rupali S. Pardhi,Sanjeevani A. Hiradkar, Dhanshri S. Borkar, Prof. Sonia V. Hokam, "Adaptive Smart Antenna using Neural Network (SMI Algorithm)", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.27-29, January-February-2017
URL : http://ijsrst.com/NCAEAS2308