Effective Minimization of Side Lobe Level of Sparse Thinned Planar Array Antenna in Multiple Planes with Constraints

Dutta, Kailash Pati; Mahanti, G. K.; Panda, Ganapati

doi:10.1080/02726343.2021.1928374

Cited by 3 publications

(7 citation statements)

References 21 publications

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“…In this work, two well‐performing algorithms, namely, symbiotic organism search (SOS) 37 and moth‐fly optimization (MFO) 38,39 algorithms for the effective synthesis of sparse planar array with constraints are implemented and compared under fairly similar conditions. Both SOS and MFO are the well‐known meta‐heuristic algorithms which have been gainfully and effectively applied to diverse engineering optimization problems 24,29,30,37,38 . The performance of various other meta‐heuristic algorithms is largely dependent upon the tunable algorithm‐specific control parameters.…”

Section: Introductionmentioning

confidence: 99%

“…This study discusses the element position optimization in a sparse array with rectangular boundary. The purpose of this synthesis problem is to place multiple small and wide nulls simultaneously in the radiation pattern along with the optimization of the SLL in the principal azimuth plane 27–36 . This paper holds manifold contributions.…”

Section: Introductionmentioning

confidence: 99%

“…23 A number of drawbacks are allied with these methods like lack of performance efficiency and inadequacy in design of array structures with poor null control. To overcome these problems, recently many researchers have successfully used nature inspired meta-heuristic algorithms 24 which are not only versatile but give more insights for efficient null control with optimal array characteristics, 13,[25][26][27][28][29][30][31][32][33][34][35][36] for examples, genetic algorithm (GA), 25,26 fruit-fly optimization algorithm (FOA), 13 modified iterative Fourier transform and hybrid genetic algorithm, 27 perturbed compressive sampling (PCS) method, 28 crow search algorithm, 29 teaching learning-based optimization (TLBO), 29,30 Schelkunoff theory, 34 and gravitational search algorithm (GSA). 32 Though, the aforesaid papers based on the stochastic optimization methods are presented successfully, but most of them lack generality and fair comparison.…”

mentioning

confidence: 99%

“…Both SOS and MFO are the well-known meta-heuristic algorithms which have been gainfully and effectively applied to diverse engineering optimization problems. 24,29,30,37,38 The performance of various other meta-heuristic algorithms is largely dependent upon the tunable algorithm-specific control parameters. Selection operator, crossover probability, and mutation probability in case of GA and search coefficient plus weighting coefficient in case of FOA are some of the examples of such tunable control parameters.…”

mentioning

confidence: 99%

“…The purpose of this synthesis problem is to place multiple small and wide nulls simultaneously in the radiation pattern along with the optimization of the SLL in the principal azimuth plane. [27][28][29][30][31][32][33][34][35][36] This paper holds manifold contributions. First, the novel implementation of SOS and MFO in the synthesis of sparse planar array.…”

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confidence: 99%

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Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

Dutta

Misra

Mahanti

2022

Int J Communication

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Summary The optimization of the non‐uniform planar array antenna parameter in order to minimize the signal interference in the electromagnetic environment through null placement is one of the most important areas of investigation in the field of wireless communication. This study deals with an effective method of synthesis of a quadrant symmetric sparse planar array antenna using the novel implementation of the well‐known meta‐heuristic optimization algorithms, namely, symbiotic organism search (SOS) and moth flame optimization (MFO) algorithms. The objective of this work is to place multiple small and wide nulls at the desired angles and simultaneously minimize the side lobe levels using a nonlinear mathematical approach. Three examples are presented with constraints that include the inter‐elemental distance, fixed number of array elements in the aperture, and the aperture size. The radiating antenna array elements are considered to have fixed uniform current distribution. The meta‐heuristic optimization approach offers the advantages of avoiding the infeasible solution while finding the best optimal set of element positions to reduce side lobe level in the desired radiation pattern. The radiation pattern characteristics and the computation time associated with each example and each algorithm are recorded and compared for arriving at the conclusion. The superiority of SOS is found in terms of the optimized side lobe levels. At the same time, both algorithms are found to be efficient in positioning the symmetric nulls in desired locations in the radiation pattern successfully. When compared with the published papers, proposed approach yields better results confirming the effectiveness of the performance of these algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

Dutta

Misra

Mahanti

2022

Int J Communication

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Planar Antenna Arrays Beamforming Using Various Optimization Algorithms

et al. 2023

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Optimization Design for Sparse Planar Array in Satellite Communications

He¹,

Wang²

2023

Electronics

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The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages of this technology include low cost, low system complexity, and robust anti-interference ability, which have attracted widespread attention within the industry. In this paper, we investigate an optimization design of sparse planar arrays in satellite communication scenarios. Firstly, we introduce the mathematical foundation of the array antennas and establish the optimization design model of the sparse planar array. Secondly, we analyze and compare the impact of different array layout methods on the sparse planar array antenna pattern, and then introduce the latest design trend of array material design. Thirdly, we review some classical optimization methods for optimizing sparse planar arrays and the recent research advancements in promising and novel methods. Lastly, on the basis of the present research status, we propose three future research directions and two critical challenges for optimal design of sparse planar arrays in satellite communication scenarios, which can facilitate the development and realization of array technology under future B5G and 6G wireless networks.

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Effective Minimization of Side Lobe Level of Sparse Thinned Planar Array Antenna in Multiple Planes with Constraints

Cited by 3 publications

References 21 publications

Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

Planar Antenna Arrays Beamforming Using Various Optimization Algorithms

Optimization Design for Sparse Planar Array in Satellite Communications

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