Proceedings National Graduate Conference 2012 (NatGrad2012), Universiti Tenaga Nasional, Putrajaya Campus, 8-10 Nov 2012 Overview of Particle Swarm Optimization (PSO) for Smart Antenna Beamforming Soodabeh Darzi 1 , Tiong Sieh Kiong 2 , Balasem.S.S 2 ,Mohammadreza Aghaei 1 1 Faculty of Electrical Engineering, Universiti Tenaga Nasional 2 Center of System and Machine Intelligence, Universiti Tenaga Nasional Mohammadreza Aghaei Abstract: The current paper attempts to investigate the application of Particle swarm optimization (PSO) in the Linear Constraint Minimum Variance (LCMV) beamforming technique that improves the signal to noise ratio, data rates, null steering and coverage of the cellular system. LCMV forms its radiation beam towards desired signal through its weight vector which is computed through received signal. However, weights computed by LCMV usually do not able to form the radiation beam towards the target user precisely. Hence, in this research work, Particle swarm optimization (PSO) is incorporated into the existing LCMV technique in order to improve the weights of LCMV. PSO is known as a heuristic robust stochastic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking, which applies the concept of social interaction to problem solving in various fields such as physics, chemistry, economics and engineering where the goal is to maximize efficiency or other kinds of variables. The PSO technique optimizes the particles’ objectives by updating the velocity and position of each particle based on best fitness function and moving particle swarm in the area with higher objective function value. Eventually, all particles will gather around the point with the highest objective value. This paper presents the method of optimizing LCMV’s weights using PSO. Keywords – Beamforming; smart antenna; linear constraint minimum variance (LCMV); Particle swarm optimization (PSO) I. INTRODUCTION Today, Optimization plays a significant key role in a mathematical discipline that concerns the finding of minima and maxima of functions or process. The optimization consist of different of techniques that improve the throughput of system that used in the Operations Research, artificial intelligence and computer science, and also is used to improve business processes in practically all industries. One of the optimization techniques is Particle Swarm Optimization (PSO) that is fast, simple one and need just few parameters (Kennedy et al. 2002)[1] and searches in given space of problem to find the parameters required to maximize the objective. This technique, first described by James Kennedy and Russell C. Eberhart in 1995 [2] based on certain of members animals such as fish schools and bird flocks.In PSO, each "bird" is"particle", which are randomly initialized and freely move across search space. Each of particles has position, velocity and fitness value which is evaluated by the fitness function to be optimized. During optimization, it updates its own velocity and position based on the best population. The updating move particle swarm toward the region with the higher objective function value, and finally all particles will gather around the point with the highest objective value called the global best fitness, and the candidate solution that achieved this fitness, called the global best position or global best candidate solution. [3]. (Shi and Eberhart 1998). In the past two decades the number of mobile users is increasing day by day. Smart Antenna, also known as multiple antenna, adaptive array antenna is a new technology that used to increase the efficiency of digital wireless communication systems. [4]. Beamfrming in Smart antenna is recognized as a promising technology for higher user capacity in 3G wireless networks by effectively reducing multipath and co-channel interference. [5] Beamforming is an advanced signal processing technique which, when employed along with an array of transmitters or receivers will be capable of controlling the 'directionality of' or 'sensitivity to' a particular radiation pattern. This method creates the radiation pattern of the antenna array by adding the phases of the signals in the