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

Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique.

In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA

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

L'Écho : grand quotidien d'information du Centre Ouest

21 novembre 19341934/11/21 (A63).Appartient à l’ensemble documentaire : PoitouCh

L'Écho : grand quotidien d'information du Centre Ouest

14 octobre 19431943/10/14 (A72,N625).Appartient à l’ensemble documentaire : PoitouCh

Minimization result of benchmark functions in Table 1 with <i>t</i>_<i>max</i> = 500.

<p>Minimization result of benchmark functions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140526#pone.0140526.t001" target="_blank">Table 1</a> with <i>t</i>_<i>max</i> = 500.</p

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum

<div><p>An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (<i>α</i>), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.</p></div

Comparison of <i>SINR</i> calculation for conventional MVDR, SGSA-MVDR and SL-GSA-MVDR for user at 0° and interference at 30°, 50°, 25° and 60°.

<p>Comparison of <i>SINR</i> calculation for conventional MVDR, SGSA-MVDR and SL-GSA-MVDR for user at 0° and interference at 30°, 50°, 25° and 60°.</p

Minimization result of benchmark functions in Table 1 with <i>t</i>_{<i>max</i> =} 1000.

<p>Minimization result of benchmark functions in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140526#pone.0140526.t001" target="_blank">Table 1</a> with <i>t</i>_{<i>max</i> =} 1000.</p