Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program

Application of Particle Swarm Optimization in Linear Constraint Minimum Variance Beamforming Technique

Abstract: Smart antenna that transmits and receives the signal as the beam form is a part of cellular system in wireless communication. One of the beamforming techniques that employ in smart antenna is the Linear Constraint Minimum Variance (LCMV) beamforming. The LCMV beamforming technique forms its radiation beam towards desired signal through its weight vector which is computed through received signal. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely. Hence, LCMV technique must be improved to support the system with higher quality. To solve this problem, in this paper Particle Swarm Optimization (PSO) is incorporated into the existing LCMV technique in order to improve the weights of LCMV, Signal to Interference Noise Ratio (SINR), throughput of system and data rate. This study presents the results from analysis of the designed model and general characteristic of that and presents a graphic analysis used to evaluate the appropriateness of the model parameters and the overall goodness-of-fit of the model. The obtained results in this study, which is the optimized output of LCMV beamforming, are simulation by comparing the output results in different scenarios

Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals