Performance Comparison of FA, PSO and CS application in SINR Optimisation for LCMV Beamforming Technique

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Doroody C.
Kiong T.S.
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Springer New York LLC
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A beamforming technique called Linearly Constraint Minimum Variance (LCMV) allows directing a radiation beam towards the desired direction to minimise interference of the signal through weight vectors that are computed by LCMV. Generally, to achieve a favourable beam shape, LCMV�s weights are not exactly steered towards the user�s direction. In addition, traditional methods are not equipped well to seamlessly improve the weights of LCMV. This paper employs Particle Swarm Optimisation (PSO), Firefly Algorithm (FA) and Cuckoo Search (CS) to optimise the weights of LCMV. The key anticipated goal in LCMV optimisation is the power reduction on the interferences� side to achieve a favourable beam shape and better SINR output. A common metaheuristic algorithm is Particle Swarm Optimisation (PSO), which deals with the social behaviour of creatures such as bird flocking. A population and attraction-based algorithm is employed in Firefly algorithm; the flashing characteristics of fireflies are the inspiration of the swarm intelligence metaheuristic algorithm. Also, a novel equation-based nature inspired algorithm is Cuckoo Search (CS), which is based on the brood parasitism of a few cuckoo species combined with the so-called L�vy flights. Based on simulation results, FA showed enhanced ability to precisely determine power allocation�s optimal direction when compared with CS and PSO. Thus, better SINR results could be achieved with FA. For SINR optimisation using the LCMV technique, the effectiveness of CS in comparison with FA and PSO algorithms was simulated employing MATLAB�. � 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Beamforming; Bioluminescence; Fire protection; MATLAB; Signal interference; Spurious signal noise; Beamforming technique; Firefly algorithms; LCMV; Meta heuristic algorithm; Nature inspired algorithms; Optimisations; Particle swarm optimisation; Performance comparison; Particle swarm optimization (PSO)