Linear Antenna Array Synthesis to Reduce the Interference in the Side Lobe Using Continuous Genetic Algorithm

Author(s):  
Smita Banerjee ◽  
Ved Vyas Dwivedi

Nowadays, low-side lobe antenna arrays are used in many communications systems such as satellite, cellular, radar and wireless communications. The antenna array with low side lobe rates should be designed to avoid noisy contact. A new stochastic approach to synthesize a linear antenna array to suppress normal distributed invasive weed optimization (NDIWO) is proposed in this paper synthesize a linear antenna array to suppress the side lobe levels. NDIWO is applied for optimization of the positions of the antenna elements. A 28-element linear array is designed and synthesized by using the proposed and other popular evolutionary algorithms. The acquired radiation designs are gathered with the calculations like particle swarm optimization (PSO) and differential evolution (DE). The numerical results illustrate that the NDIWO optimized antenna array performs superior over PSO and DE optimized arrays in terms of low PSLL and convergence properties.


2011 ◽  
Vol 08 (02) ◽  
pp. 171-179
Author(s):  
T. S. JEYALI LASEETHA ◽  
R. SUKANESH

This paper discusses the deployment of Genetic Algorithm optimization method for the synthesis of antenna array radiation pattern in adaptive beamforming. The synthesis problem discussed is to find the weights of the Uniform Linear Antenna array elements that are optimum to provide the radiation pattern with maximum reduction in the sidelobe level. This technique proved its effectiveness in improving the performance of the antenna array.


2021 ◽  
Author(s):  
Ali Durmus ◽  
Rifat KURBAN ◽  
Ercan KARAKOSE

Abstract Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with low side lobe level (SLL) at a desired half power beam width (HPBW) in far-field. The amplitude and position values ​​of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Mayfly algorithm (MA) and Jellyfish Search (JS) algorithms are compared to realize optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10, 16, 24 and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers and statistical results show that performance of the novel algorithms, MA and JS, are better than well-known methods PSO and ABC.


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