Sparse array antenna optimization using genetic alghoritms

Deparateanu, Daniel; Enache, Florin; Enache, Andrei; Popescu, Florin; Nicolaescu, Ioan

doi:10.1109/ecai.2016.7861100

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…It is a kind of evolutionary search algorithm to give solutions to optimization problems based on natural selection [5]. It starts with initializing the population of possible solutions by natural random selection, solutions are nothing but chromosomes [16]. The array of genes is called chromosomes, genes are the basic building blocks of the genetic algorithm [23].…”

Section: ░ 2 Genetic Algorithmmentioning

confidence: 99%

Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

Ramya.

Raju

2023

IJEER

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In the field of pattern synthesis, the design of arrays to synthesis optimized sum and difference patterns in a successive manner is an important problem in most of the radars. Although several researchers published many papers in the open literature, investigations are made by the authors to design arrays of discrete radiators using a well-formulated Genetic Algorithm (GA). The practical constraints are taken into account to design an amplitude distribution to produce the sum pattern with desired side lobe levels without broadening the main beam. Such sum patterns are found to be very useful in high-resolution radars where the EMI problems are also addressed. In IFF radars, both sum and difference patterns are extremely useful to distinguish between friendly aircraft and enemy aircraft. Such patterns are conventionally produced involving two antennas. One is a directional antenna and another is an omnidirectional antenna. However, in the present work, they are generated using only one directional antenna. The patterns with a deep null in the boresight direction and difference lobes with a high slope are produced with an antiphase excitation to one-half of the array.

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Section: ░ 2 Genetic Algorithmmentioning

confidence: 99%

Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

Ramya.

Raju

2023

IJEER

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“…Several evolutionary algorithms have been successfully used to optimize antenna array but yet there is a need for better performance; thus, past algorithms are being improved on and new ones are also developed. Here are some of the intelligent optimization algorithms that have been used for antenna array synthesis: genetic algorithms (GA) [12] [13] [14], particle swarm optimization (PSO) algorithms [15] [16], quantum particle swarm optimization (QPSO) [17], ant colony optimization (ACO) [18], selfadaptive differential evolution (SADE) [19], backtracking search optimization algorithm (BSA) [20], symbiotic organisms search (SOS) [21], compressed sensing (CS) [22], biogeography-based optimization (BBO) [23], firefly algorithm (FA) [24] [25], grey wolf optimization (GWO) [9], moth flame optimization (MFO) [26], modified wolf pack Algorithm (MWPA) [10], hybrid particle swarm optimization (PSO) and convex (CVX) optimization (PSO-CVX) [27], convex optimization [28] and several others. These algorithms have been applied on different type of antennas array which includes linear [7] [25] [21] [19] [22] [9] [26] [22], circular [29] [26], cylindrical [30], conformal [31] [32] [33], hexagonal arrays [34], time-modulated array [27] [28], and lot more.…”

Section: Introductionmentioning

confidence: 99%

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

et al. 2021

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Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

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“…This latter issue has attracted the interests of the research community since the 1960s [23][24][25][26][27], regaining attention in the more recent years thanks to its applicability to the emerging communication scenarios. Accordingly, several methods have been proposed in the literature for the synthesis of sparse antenna arrays by considering both stochastic and deterministic approaches [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Stochastic methods can rely on different strategies, including genetic algorithms [28,29], particle swarm optimization [30], differential evolution [31], and nature inspired techniques, such as the ant [32], whale [33], and grey wolf [34] optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

Buttazzoni

Babich

Vatta

et al. 2020

Sensors

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One of the main targets of the forthcoming fifth-generation (5G) cellular network will be the support of the communications for billions of sensors and actuators, so as to finally realize the Internet of things (IoT) paradigm. This pervasive scenario unavoidably requires the design of cheap antenna systems with beamforming capabilities for compensating the strong attenuations that characterize the millimeter-wave (mmWave) channel. To address this issue, this paper proposes an iterative algorithm for sparse antenna arrays that enables to derive the number of elements, their amplitudes, phases, and positions in the presence of constraints on the far-field pattern. The algorithm, which relies on the compressive sensing approach, is formulated by transforming the original nonconvex optimization problem into a convex one. To prove the suitability of the conceived solution for 5G IoT mmWave applications, numerical examples and comparisons with other existing methods are provided, considering synthesis problems with different pattern and aperture specifications.

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Sparse array antenna optimization using genetic alghoritms

Cited by 7 publications

References 7 publications

Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications

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