Publication: SINR improvement using Firefly Algorithm (FA) for Linear Constrained Minimum Variance (LCMV) beamforming technique
Date
2015
Authors
Doroody C.
Tiong S.K.
Darzi S.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
This paper presents the significance of the application of Firefly Algorithm (FA) in the Linearly Constrained Minimum Variance (LCMV) beamforming technique. In this work, in order to obtain the best result, different amounts of parameters such as iteration, null and the boundary have been implemented for more than 30 times. This is done to improve the null steering, signal to interference plus noise ratio (SINR), direction detection and power consumption efficiency. The LCMV beamformer directs the beam radiation through the desired point of user using allocated weight vectors derived from the received signal. However, the weights derived from the received signal by the LCMV method (WLCMV) are not usually focused on the users point accurately. Thus, there will be significant power wastage in other directions rather than the desired users point. Accordingly, here the FA technique is applied into the LCMV beamformer to modify the weights of LCMV. The FA is a metaheuristic algorithm based on the flashing lights of fireflies. It has perfectly solved many problems in almost all engineering fields, such as, image processing, industrial optimization, antenna design and business optimization, etc. The FA method optimizes the light intensity of fireflies by updating the attractiveness and the position of each firefly based on the brightest firefly or the highest light intensity value. Ultimately, all fireflies will gather around the firefly with the highest light intensity value. In this research, results achieved from the optimization of LCMV using FA are simulated in several cases. � 2015 IEEE.
Description
Antennas; Arts computing; Beamforming; Bioluminescence; Energy efficiency; Fire protection; Image processing; Optimization; Signal interference; Signal to noise ratio; Beamforming technique; Business optimization; Firefly algorithms; Industrial optimization; Linear constrained minimum variances; Linearly constrained minimum variance; Meta heuristic algorithm; Signal to interference plus noise ratio; Iterative methods