Modeling and Designing of a Compact Single Band PIFA Antenna for Wireless Application Using Artificial Neural Network

Sellak, Lahcen; Aguni, Lahcen; Chabaa, Samira; Ibnyaich, Saida; Zeroual, Abdelouhab; Baddou, Atmane

doi:10.1007/s11277-022-09912-7

Cited by 3 publications

(1 citation statement)

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“…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%

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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