Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays

Zhang, Jiahao; Akinsolu, Mobayode O.; Liu, Bo; Vandenbosch, Guy A. E.

doi:10.1109/tap.2020.3012792

Cited by 35 publications

(23 citation statements)

References 18 publications

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“…This was done using an inhouse developed PSADEA algorithm. This was because PSADEA had previously been demonstrated in [20,21] to be suitable for complex antenna designs involving challenging geometric constraints and stringent performance requirements, which other standard global optimization methods are not be able to address [28], [29]. PSADEA uses a Gaussian process (GP) t o predict new values of geometry parameters based on previous evaluations [30].…”

Section: The Highly Folded Self-grounded Bowtiementioning

confidence: 99%

Dual-Polarized Highly Folded Bowtie Antenna With Slotted Self-Grounded Structure for Sub-6 GHz 5G Applications

Alibakhshikenari

Virdee

See

et al. 2022

IEEE Trans. Antennas Propagat.

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In this paper, a novel dual-polarized highly-folded self-grounded Bowtie antenna that is excited through I-shaped slots is proposed for applications in sub-6GHz 5G multiple-input-multiple-output (MIMO) antenna systems. The antenna consists of two pairs of folded radiation petals whose base is embedded in a double layer of FR-4 substrate with a common ground-plane which is sandwiched between the two substrate layers. The ground-plane is defected with two I-shaped slots located under the radiation elements. Each pair of radiation elements are excited through a microstrip line on the top layer with RF signal that is 180° out of phase with respect to each other. The RF signal is coupled to the pair of feedlines on the top layer through the I-shaped slots from the two microstrip feedlines on the underside of the second substrate. The proposed feed mechanism gets rid of the otherwise bulky balun. The Bowtie antenna is a compact solution with dimensions of 32 32 33.8 mm 3 . Measured results have verified that the antenna operates over a frequency range of 3.1-5 GHz and exhibits an average gain and antenna efficiency in the vertical and horizontal polarizations of 7.5 dBi and 82.6%, respectively.

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Section: The Highly Folded Self-grounded Bowtiementioning

confidence: 99%

Dual-Polarized Highly Folded Bowtie Antenna With Slotted Self-Grounded Structure for Sub-6 GHz 5G Applications

Alibakhshikenari

Virdee

See

et al. 2022

IEEE Trans. Antennas Propagat.

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“…However, the CDSIC is complex and costly, since many basic components are mandatory, such as the sampling coupler, the vector modulator, and the combining coupler. Compared with the CDSIC, the FDSIC normally has a simpler structure [57][58][59].…”

Section: Operating Mechanism Of the Fdsicmentioning

confidence: 99%

Self-Interference Cancellation: A Comprehensive Review from Circuits and Fields Perspectives

Zhang¹,

He²,

Li³

et al. 2022

Electronics

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Increased demand for higher spectrum efficiency, especially in the space-limited chip, base station, and vehicle environments, has spawned the development of full-duplex communications, which enable the transmitting and receiving to occur simultaneously at the same frequency. The key challenge in this full-duplex communication paradigm is to reduce the self-interference as much as possible, ideally, down to the noise floor. This paper provides a comprehensive review of the self-interference cancellation (SIC) techniques for co-located communication systems from a circuits and fields perspective. The self-interference occurs when the transmitting antenna and the receiving antenna are co-located, which significantly degrade the system performance of the receiver, in terms of the receiver desensitization, signal masking, or even damage of hardwares. By introducing the SIC techniques, the self-interference can be suppressed and the weak desired signal from the remote transmitter can be recovered. This, therefore, enables the full-duplex communications to come into the picture. The SIC techniques are classified into two main categories: the traditional circuit-domain SICs and the novel field-domain SICs, according to the method of how to rebuild and subtract the self-interference signal. In this review paper, the field-domain SIC method is systematically summarized for the first time, including the theoretical analysis and the application remarks. Some typical SIC approaches are presented and the future works are outlooked.

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“…ML has become popular with reconfigurable antennas, specifically frequency-reconfigurable ones [102,103]. For example, an intelligent surrogate model-assisted differential evolution to synthesize an antenna array [24] with reconfigurable frequency was reported earlier. ML has been shown to be more robust in complex environments compared to most other signal processing techniques and offered an increased signal-to-noise ratio and efficient beamforming architectures.…”

Section: Antenna Position Direction and Radiation Estimationmentioning

confidence: 99%

“…Requires large dataset, may be computationally costly and complex with hybrid compound structures of the model [24,…”

Section: Future Scopementioning

confidence: 99%

“…ML has simplified the maintenance and control of fabrication systems to achieve significant miniaturization [23]. Various automated and cost-effective design procedures using ML techniques have been used to develop complex antenna technologies [24,25]. Moreover, different fault detection in large antenna-array-based knotty systems have also been resolved by ML.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays

Abstract: The material cannot be used for any other purpose without further permission of the publisher and is for private use only. There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 35 publications

References 18 publications

Dual-Polarized Highly Folded Bowtie Antenna With Slotted Self-Grounded Structure for Sub-6 GHz 5G Applications

Dual-Polarized Highly Folded Bowtie Antenna With Slotted Self-Grounded Structure for Sub-6 GHz 5G Applications

Self-Interference Cancellation: A Comprehensive Review from Circuits and Fields Perspectives

Application of Machine Learning in Electromagnetics: Mini-Review

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