Bandwidth optimization of compact microstrip antenna for PCS/DCS/bluetooth application

Singh, Vinod Kumar; Ali, Zakir; Ayub, Shahanaz; Singh, Ashutosh Kumar

doi:10.2478/s13531-013-0160-3

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…Fabric antennas require a conducting material to function as a radiator, which is essential for their electrical characteristics [3]; thus, the electric and electromagnetic features of the materials are crucial in antenna design. Alternately, microstrip patch antennas are lightweight and robust, with a low fabrication cost, and are easy to integrate with radio frequency; hence, they are a good candidate for body-worn applications [5].…”

Section: Dielectric Permittivity Parameter Measurementmentioning

confidence: 99%

Key Factors in the Implementation of Wearable Antennas for WBNs and ISM Applications: A Review WBNs and ISM Applications: A Review

et al. 2022

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The increasing usage of wireless technology has prompted the development of a new generation antenna compatible with the latest devices, with on-body antennas (wearable antennas) being one of the revolutionary applications. This modern design is relevant in technologies that require close human body contact, such as telemedicine and identification systems, due to its superior performance compared to normal antennas. Some of its finer characteristics include flexibility, reflection coefficient, bandwidth, directivity, gain, radiation, specific absorption rate (SAR), and efficiency that are anticipated to be influenced by the coupling and absorption by the human body tissues. Furthermore, improvements like band-gap structure and artificial magnetic conductors (AMC) and (DGS) are included in the wearable antenna that offers a high degree of isolation from the human body and significantly reduces SAR. In this paper, the development of on-body antennas and how they are affected by the human body were reviewed. Additionally, parameters that affect the performance of this new antenna model, such as materials and common technologies, are included as an auxiliary study for researchers to determine the factors affecting the performance of the wearable antenna and the access to a highly efficient antenna.

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Section: Dielectric Permittivity Parameter Measurementmentioning

confidence: 99%

Key Factors in the Implementation of Wearable Antennas for WBNs and ISM Applications: A Review WBNs and ISM Applications: A Review

et al. 2022

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“…However, the solution is not convincing owing to the complicated design process, power limitation, and standardization. Other approaches engaging optimization techniques also exist, but most have focused on sizing adjustment of the antenna [30]. Even though the sizing optimization can draw simple and efficient antenna update [31], the technique forces the antenna to maintain its original shape, which interrupts a fundamental solution regarding the narrow bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

et al. 2020

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Interests in strain gauge sensors employing stretchable patch antenna have escalated in the area of structural health monitoring, because the malleable sensor is sensitive to capturing strain variation in any shape of structure. However, owing to the narrow frequency bandwidth of the patch antenna, the operation quality of the strain sensor is not often assured under structural deformation, which creates unpredictable frequency shifts. Geometric properties of the stretchable antenna also severely regulate the performance of the sensor. Especially rugged substrate created by printing procedure and manual fabrication derives multivariate design variables. Such design variables intensify the computational burden and uncertainties that impede reliable analysis of the strain sensor. In this research, therefore, a framework is proposed not only to comprehensively capture the sensor’s geometric design variables, but also to effectively reduce the multivariate dimensions. The geometric uncertainties are characterized based on the measurements from real specimens and a Gaussian copula is used to represent them with the correlations. A dimension reduction process with a clear decision criterion by entropy-based correlation coefficient dwindles uncertainties that inhibit precise system reliability assessment. After handling the uncertainties, an artificial neural network-based surrogate model predicts the system responses, and a probabilistic neural network derives a precise estimation of the variability of complicated system behavior. To elicit better performance of the stretchable antenna-based strain sensor, a shape optimization process is then executed by developing an optimal design of the strain sensor, which can resolve the issue of the frequency shift in the narrow bandwidth. Compared with the conventional rigid antenna-based strain sensors, the proposed design brings flexible shape adjustment that enables the resonance frequency to be maintained in reliable frequency bandwidth and antenna performance to be maximized under deformation. Hence, the efficacy of the proposed design framework that employs uncertainty characterization, dimension reduction, and machine learning-based behavior prediction is epitomized by the stretchable antenna-based strain sensor.

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“…Microstrip antenna (MSA) includes a conductive ground plane, a radiation patch and a dielectric substrate between them [1]. As it is featuring small size, light weight, low profile and simple in making, MSA has been a hot topic in communication systems [2][3][4]. As we all know, the modelling of MSA is always done in fullwave simulation software such as HFSS, CST, IE3D and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model

Chen

Tian

Qiang

et al. 2018

IET Microwaves, Antennas & Propagation

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When microstrip antennas (MSAs) are optimised, the full‐wave electromagnetic simulation takes long time to get the result which is very time consuming. Hence, the knowledge‐based neural network (KBNN) is used here to replace the electromagnetic simulation to shorten the calculating time and raise efficiency. However, prior knowledge of KBNN is always obtained by empirical formulas and neural networks, both of them are heavy and complicated. In this study, Gaussian process regression (GPR) is proposed to get the prior knowledge. The modelling of MSA is realised by the prior knowledge input method. The short optimal time and excellent optimal results prove that the proposed KBNN is a fast and effective method for the optimisation of MSAs.

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Bandwidth optimization of compact microstrip antenna for PCS/DCS/bluetooth application

Cited by 19 publications

References 13 publications

Key Factors in the Implementation of Wearable Antennas for WBNs and ISM Applications: A Review WBNs and ISM Applications: A Review

Key Factors in the Implementation of Wearable Antennas for WBNs and ISM Applications: A Review WBNs and ISM Applications: A Review

Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model

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