Design of a Symmetric CPW-Fed Patch Antenna for WLAN/WIMAX Applications Using ANN

Aguni, Lahcen; Yassini, Abdessalam El; Chabaa, Samira; Ibnyaich, Saida; Zeroual, Abdelouhab

doi:10.1007/s11277-020-07580-z

Cited by 20 publications

(5 citation statements)

References 17 publications

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“…This configuration is favored for this work due to its simple impedance matching and the high circuit density on a single layer. In conjunction with this, its active and passive elements are easy to integrate and endure with minimal dispersion and radiation loss [17]. All these features make the CPW an appropriate solution for rectifying designs.…”

Section: Antenna Design Configurationmentioning

confidence: 99%

A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications

Nuraiza Ismail,

Ermeey Abd Kadir

2023

Int. j. eng. technol. innov.

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This study proposes an ultra-wideband antenna for ambient radio frequency (RF) energy harvesting applications. The antenna is based on a co-planar waveguide (CPW) transmission line and incorporates a rectangular slot as an antenna harvester. The proposed antenna utilizes an evolutionary design process to achieve impedance matching of the 50 Ω CPW feeding line over the desired frequency bands. A parametric study investigates CPW elements and rectangular slot size. The harvester antenna is then connected to the primary rectifier circuit of the voltage doubler to examine the signal characteristics. The antenna covers the Industry, Science, and Medicine (ISM) Wi-Fi bands of 2.45 GHz and 5 GHz, achieving a realized gain of 3.641 dBi and 4.644 dBi at 2.45 GHz and 5 GHz, respectively. It exhibits a relatively broad frequency ranging from 2.16 GHz to 6.32 GHz, covering the ultra-wideband fractional bandwidth (FBW) of 105%.

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Section: Antenna Design Configurationmentioning

confidence: 99%

A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications

Nuraiza Ismail,

Ermeey Abd Kadir

2023

Int. j. eng. technol. innov.

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“…Because the prediction of DBN-ELM model is basically consistent with the simulation results of HFSS, so the proposed method can replace HFSS The above results show that the optimized MIMO antenna is simple, compact, and has good port isolation performance. The antenna structure designed in this paper is more complex compared to the literature [33], which indicates that this method can be applied to more complex model structures.…”

Section: Parametersmentioning

confidence: 99%

Design of UWB Antenna Based on Improved Deep Belief Network and Extreme Learning Machine Surrogate Models

Nan

Xie²,

Gao

et al. 2021

IEEE Access

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In the design of conventional microwave devices, the parameters need to be continuously optimized to meet the desired targets, and the whole process is time-consuming and laborious. As a surrogate model, machine learning is an effective optimization method. However, in the modeling process, the highdimensional data processing and the complex nonlinear relationship between parameters is a problem to be solved. This paper proposes a deep learning model for designing UWB antennas, which determines the model structure of deep belief network (DBN) by particle swarm algorithm (PSO), and then combines DBN and extreme learning machine (ELM). The proposed model can obtain higher feature learning capability and nonlinear function approximation capability, and has been applied to the optimal design of the whole structure of the fractal antenna and the notch structure of the MIMO antenna, and its S-parameters are well fitted while meeting the requirements of the design targets. The DBN-ELM method obtains the good results when compared with common modeling methods using the same training samples (the root mean square error tested is 11.87% in the fractal antenna and 3.56% in the MIMO antenna). Overall, the proposed DBN-ELM model has higher predictive and generalization capabilities, which can also be used to model more complex antenna structures. INDEX TERMSDeep belief network (DBN),Extreme learning machine (ELM),Particle swarm optimization (PSO),UWB antenna.

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“…Compared to FPDM, IPDM is more in line with the public's needs. It has a large number of application cases in antenna design [20][21][22][23][24][25]. These applications have similarities with the design of metasurfaces [19,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Hu,

Ma,

Zhang

et al. 2023

J. Phys. D: Appl. Phys.

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To solve the time-consuming and complex design problems, the deep learning method is used to realize the inverse predictive design of a transmission-type linear-to-circular polarization control metasurface (TLCPCM). Firstly, the target-generation neural net-work model (TGNNM) is constructed based on a fully connected neural network (FCNN). The model selects the critical features of the required electromagnetic perfor-mance as design targets, and maps low-dimensional design targets to high-dimensional electromagnetic performance. Secondly, taking the output data of the TGNNM as input data, an inverse-mapping neural network model (IMNNM) is constructed by a convolu-tional neural network (CNN). The prediction performance of the IMNNM is compared with two other inverse-mapping models. The research results show that the IMNNM out-performs the other two networks. Finally, combining TGNNM and IMNNM, four sets of TLCPCM structural parameters are predicted. The research results show that the elec-tromagnetic performances of the metasurface determined by the predicted structural parameters are generally consistent with the given design targets. On this basis, one ex-perimental sample is manufactured. The measurement results are consistent with the simulation results. The research results demonstrate the validity and feasibility of the inverse predictive design method proposed in this paper.

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Design of a Symmetric CPW-Fed Patch Antenna for WLAN/WIMAX Applications Using ANN

Cited by 20 publications

References 17 publications

A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications

A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications

Design of UWB Antenna Based on Improved Deep Belief Network and Extreme Learning Machine Surrogate Models

Inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

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