Home > Archives > NCAEAS2342 IJSRST-Library

Adaptive Smart Antenna Using Neural Network (LSMI Algorithm)

Authors(5) :-Rupali S. Pardhi, Bhagyashri B. Hedau, Megha V. Dhamgaye, Kalyani P. Mohane, Prof. Sonia V. Hokam

The adaptive algorithm used in the signal processing has profound effect on the performance of a Smart Antenna system that is known to have resolutions and interference rejection capability when array steering vector is precisely known. Adaptive beam forming is used for enhancing a desired signal while suppressing noise and interference at the output of an array of sensors. However the performance degradation As the growing demand for mobile communication is constantly increasing, the need for better coverage, improved capacity, and higher transmission quality rises. Thus, a more efficient used of the radio spectrum is required. A smart antenna system is capable of efficiently utilizing the radio spectrum and is a promise for an effective solution to the present wireless system problem while achieving reliable and robust high speed, high data rate transmission. Smart antenna technology offer significantly improved solution to reduce interference level and improved system capacity. With this technology, each user’s signal is transmitted and received by the base station only in the direction of that particular user. Smart antenna technologies attempts to address this problem via advanced signal processing technology called beam forming of adaptive beam forming may become more pronounced than in an ideal case because some of underlying assumption on environment, source or sensor array can be violated and this may cause mismatch. Ther are several efficient approaches that provide and improved robustness against mismatch as like LSMI algorithm.
Rupali S. Pardhi, Bhagyashri B. Hedau, Megha V. Dhamgaye, Kalyani P. Mohane, Prof. Sonia V. Hokam
  1. Widrow B et al. Adaptive antenna systems. The Journal of the Acoustical Society of America. 1967; 42.5:1175–6.
  2. Widrow B, Mantey P, Griffiths L, et al. Adaptive Antenna Systems. Proceedings of the IEEE. 1967 Dec; 55.
  3. Burg JP. The Relationships between Maximum Entropy Spectra and Maximum Likelihood Spectra. Geophysics. 1972 Apr; 37:375–6.
  4. Monzingo, R., and T. Miller,Introduction to Adaptive Arrays, Wiley Interscience, John Wiley & Sons, New York, 1980.
  5. Jeffrey H. Reed “Software Radio” A modern approach to Radio engineering.
  6. Winters J. Smart Antenna Techniques and their Application to Wireless Ad-hoc Networks. IEEE Wireless Communications. 2006; 13(4).
Publication Details
  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) : 175-177
Manuscript Number : NCAEAS2342
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Rupali S. Pardhi, Bhagyashri B. Hedau, Megha V. Dhamgaye, Kalyani P. Mohane, Prof. Sonia V. Hokam, "Adaptive Smart Antenna Using Neural Network (LSMI Algorithm)", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.175-177, January-February-2017
URL : http://ijsrst.com/NCAEAS2342