Design of DGS Compact UWB Antenna for C-, X-, Ku-, and Ka-Band Applications Using ANN and ANFIS Optimization Techniques

Maurya, Rakesh Kumar; Kannaujia, Binod Kumar; Maurya, Ajay Kumar; Prakash, Ravi

doi:10.1007/978-981-16-6246-1_1

Cited by 4 publications

(5 citation statements)

References 34 publications

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“…Figures 23(c) and 23(d) further present the measured reflection and transmission coefficients, offering valuable experimental insights into the antenna's performance. As shown, the simulation bandwidth is specified as 20 GHz (20-40 GHz), while the measured bandwidth is reported as 15 GHz (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). The observed shift between the simulated and measured reflection coefficients within the frequency range of 20 to 40 GHz can be primarily attributed to the intricate challenges associated with the fabrication process and the measurement setup, particularly at the mmWave frequencies.…”

Section: Measured S-parametersmentioning

confidence: 98%

“…In recent years, the research community has turned to various soft computing techniques to facilitate the development and evaluation of antennas, aimed at accelerating the design process. Among these techniques, artificial neural networks (ANN) have emerged as a promising avenue for addressing these challenges [17,18], adaptive neuro-fuzzy inference system (ANFIS ) [19][20][21], particle swarm optimization (PSO) [22], genetic algorithm (GA) [23], and radial basis function neural network (RBFNN) [24]. This paper introduces a streamlined and compact cross-shaped slot broadband antenna, complemented by a 4 × 4 MIMO configuration.…”

Section: Introductionmentioning

confidence: 99%

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RBFNN-based UWB 4 × 4 MIMO antenna design with compact size, high isolation, and improved diversity performance for millimeter-wave 5G applications

Sellak,

Khabba,

Chabaa

et al. 2024

Phys. Scr.

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The millimeter-wave spectrum has emerged as a compelling solution to address the pressing need for high-dataratecapabilities in the development of 5G technology systems. Spanning between 20 GHz and 40 GHz, this spectrumencompasses several prominent frequency bands crucial for advancing 5G applications. In light of this, our studypresents a thorough investigation into the design and performance of a compact cross-shaped slot broadband antenna,complemented by a 4×4 Multiple-InputMultiple-Output (MIMO) configuration tailored for 5G operations at28 GHz. The primary objective of this study is to develop an antenna system capable of achieving an extended bandwidthranging from 20 GHz to 40 GHz, effectively covering the crucial frequency bands essential for 5G millimeterwave(mmWave) operations. To accomplish this, the optimization of antenna performance is meticulously carried outusing the Radial Basis FunctionNeuralNetworks (RBFNN) model. The RBFNNmodel serves as a robust tool for establishingthe intricate relationship between antenna dimensions, resonant frequency, and bandwidth. Subsequently,the developed RBFNN model is employed to predict optimal antenna dimensions, ensuring resonance at 28 GHz andmeeting specified bandwidth targets. The single antenna is designed with a rectangular patch and a cross-shapedslot and is constructed on the low loss Rogers RT Duroid 5880 substrate. This design reaches an outstanding bandwidthof 19.5 GHz, and exhibits excellent radiation characteristics, with a high radiation efficiency of up to 99% and acorresponding gain of 5.75 dB. The antenna’s design and performance are rigorously designed using HFSS software,which is then compared to the results acquired using CST software. In addition, the proposed MIMO configurationoffers excellent performance in terms of key features such as small size (16 × 16.2 mm2), very wide bandwidth of 20GHz, good gain of 6.75 high isolation exceeding 35 dB, and significant improvements in diversity performance measuressuch as Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL), Total ActiveReflection Coefficient (TARC), and Mean Effective Gain (MEG). The potential of the proposed MIMO configurationfor high-speed applications is particularly remarkable. Practical verification of the MIMO configuration is carefullycarried out by fabrication andmeasurement. Experimental results strongly confirmthe effectiveness of the proposedantenna design, establishing it as a competitive challenger for 5G technology.

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Section: Measured S-parametersmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

RBFNN-based UWB 4 × 4 MIMO antenna design with compact size, high isolation, and improved diversity performance for millimeter-wave 5G applications

Sellak,

Khabba,

Chabaa

et al. 2024

Phys. Scr.

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“…Data-driven surrogate modeling has proved its usage in the design procedure of highfrequency devices as a low-cost surrogate of the various electrical and field responses of high-frequency stages such as scattering parameters [S], 14,15 reflection phase characteristics in reflect-arrays, 16 characteristic impedance, 17 and prediction resonant frequency of antenna designs. [18][19][20] In each of the mentioned works, different types of Artificial Intelligence (AI) regression methods such as polynomial, 21,22 kriging, [23][24][25] Support Vector Regression (SVR), [26][27][28][29] Artificial Neural Networks (ANNs), [30][31][32][33][34] and Deep Learning (DL) [35][36][37][38][39] had been used to create an accurate, stable mapping between the given input space of the problem and the targeted characteristic as the output of the model.…”

Section: Introductionmentioning

confidence: 99%

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

Piltan

Kīzīlay

Belen

et al. 2023

Micro & Optical Tech Letters

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Horn antenna designs are favored in many applications where ultra‐wide‐band operation range alongside of a high‐performance radiation pattern characteristics are requested. Scattering‐parameter characteristics of antennas is an important design metric, where inefficiency in the input would drastically lower the realized gain. However, satisfying the requirement for scattering parameters are not enough for having an antenna with high‐performance results, where the radiation characteristic of the design can be changed independently than the scattering parameters behavior. A design might have a high‐efficiency performance, but the radiation characteristics might not be acceptable. Furthermore, there are other design considerations such as size and volume of the design alongside of these conflicting characteristics, which directly affect the manufacturing cost and limits the possible applications. In this work, by using data‐driven surrogate modeling, it is aimed to achieve a computationally efficient design optimization process for horn antennas with high radiation performance alongside of being small in or within the limits of the desired application limits. Here, the geometrical design variables, operation frequency, and radiation direction of the design will be taken as the input, while the realized gain of the design is taken as the output of the surrogate model. Series of powerful and commonly used artificial intelligence algorithms, including Deep Learning had been used to create a data‐driven surrogate model representation for the handled problem, and 80% computational cost reduction had been obtained via proposed approach. As for the verification of the studied optimization problem, an optimally designed antenna is prototyped via the use of three‐dimensional printer and the experimental results ware compared with the results of surrogate model.

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“…By leveraging data obtained from a limited number of simulations or experiments, these models facilitate the exploration of vast design spaces and enable efficient optimization, uncertainty quantification, and sensitivity analysis. Thus, it is a significantly more efficient method to perform optimization management over data driven surrogate models most especially models based on artificial intelligence (AI) algorithms such as artificial neural networks [31][32][33][34][35][36][37][38][39] or deep learning algorithms [40][41][42][43][44][45][46][47][48], instead of EM solvers. The data-driven surrogate model is used by many researchers for many applications such as parameter tuning [49][50], statistical analysis and multi objective design [54][55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Data Driven Surrogate Modeling of Phase Array Antennas Using Deep Learning for Millimetric Band Applications

Tulum,

Turk,

Mahouti

2023

IEEE Access

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Phased Array Antenna (PAA) technology plays an important role in fields such as radar, 5G and satellite or any application which requires wide bandwidth and high gain. However, achieving such design is a difficult and complex task that requires an accurate calculation and combination of results obtained for varying phase and amplitudes of each unit and coupling effects between these elements of the PAA structure is a task that can only be obtained using full wave EM simulation tools. This comes at the price of a significant increase for the computational cost of design process which is a well-known drawback of forward EM modelling of microwave stages most especially in case of repetitive analysis's such as yield analyses or optimization tasks. Data-driven surrogate models have emerged as a powerful and versatile solution that bridges the gap between computationally expensive simulations and rapid, reliable prediction models suitable for deployment in applications such as optimization and/or yield analyses. Herein, for having a high-performance broadband PAA for millimeter band in a computationally efficient manner, artificial intelligence based surrogate model assisted optimization approach is deployed. A series of state-of-the-art surrogate modelling algorithms are deployed to create a surrogate model of the studied PAA design for prediction of radiation pattern characteristic with respect to the input phase values of each array element. As a result, a drastic reduction in computational time of almost 90% for the optimization of three PAA designs is achieved. Thus, the proposed approach offers promising avenues for further exploration in computational electromagnetics, most especially in simulation expensive problems with complex designs.

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Design of DGS Compact UWB Antenna for C-, X-, Ku-, and Ka-Band Applications Using ANN and ANFIS Optimization Techniques

Cited by 4 publications

References 34 publications

RBFNN-based UWB 4 × 4 MIMO antenna design with compact size, high isolation, and improved diversity performance for millimeter-wave 5G applications

RBFNN-based UWB 4 × 4 MIMO antenna design with compact size, high isolation, and improved diversity performance for millimeter-wave 5G applications

Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

Data Driven Surrogate Modeling of Phase Array Antennas Using Deep Learning for Millimetric Band Applications

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