Adaptive Synthesis for Resonator-Coupled Filters Based on Particle Swarm Optimization

Luo, Xun; Yang, Bong-Jun; Qian, Huizhen Jenny

doi:10.1109/tmtt.2018.2878197

Cited by 30 publications

(9 citation statements)

References 24 publications

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“…A representative scenario is re-design (or dimension scaling) of a given structure for operating frequencies that are significantly shifted from those at the currently available design. Under such circumstances, gradient-based and similar routines are likely to fail, whereas resorting to global procedures (typically, population-based metaheuristics 51 , efficient global optimizers 52 , or machine learning methods 53 ) entails considerable computational expenses.…”

Section: Design Specification Management For Reliable Microwave Optimizationmentioning

confidence: 99%

Design specification management with automated decision-making for reliable optimization of miniaturized microwave components

Kozieł

Pietrenko‐Dabrowska

Płotka

2022

Sci Rep

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The employment of numerical optimization techniques for parameter tuning of microwave components has nowadays become a commonplace. In pursuit of reliability, it is most often carried out at the level of full-wave electromagnetic (EM) simulation models, incurring considerable computational expenses. In the case of miniaturized microstrip circuits, densely arranged layouts with strong cross-coupling effects make EM-driven tuning imperative to achieve the optimum performance. The process is even more challenging due to a typically large number of geometry parameters, and the lack of reasonable initial designs. The latter often encourages the use of global search procedures, which may be prohibitively expensive. In this paper, a novel automated framework for reliable optimization of miniaturized microwave components is proposed. Our methodology is based on design specification management, where the performance requirements imposed on the system are temporarily relaxed if the current design is unlikely to be improved (e.g., due to being away from the target operating frequency). The specifications are re-adjusted at each iteration of the algorithm, and eventually converge to their original values. Using two examples of compact microstrip couplers and a power divider, the presented technique is demonstrated to significantly improve the efficacy of local search routines under challenging design scenarios.

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Section: Design Specification Management For Reliable Microwave Optimizationmentioning

confidence: 99%

Design specification management with automated decision-making for reliable optimization of miniaturized microwave components

Kozieł

Pietrenko‐Dabrowska

Płotka

2022

Sci Rep

View full text Add to dashboard Cite

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“…Analytical methods have been employed to obtain the element values for specific topologies [39]- [40]. To be more general, optimization-based synthesis methods is employed for most topologies, especially when analytical synthesis is unattainable [41]- [42]. The optimization-based synthesis is more straightforward as the polynomial synthesis can be omitted since there is a direct relationship between the low-pass prototype and filter responses.…”

Section: A Filter Design Processmentioning

confidence: 99%

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Zhang

Cheng

et al. 2022

IEEE Trans. Microwave Theory Techn.

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Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate assisted microwave filters designs, this paper firstly analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) Smart data sampling techniques; 2) Advanced surrogate modeling techniques. 3) Advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in filter design are discussed.

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“…5(a), a pair of SIDGS resonant cells and T-stubs constitute the filtering part. The coupling coefficient k and external quality factor Q e of the filtering part can be obtained from the desired center frequency f 0 and 3-dB FBW [27]. k can be adjusted by the dimension d 1 and d 2 , while the dimension w 1 is the critical element to dominate Q e .…”

Section: B Fpd Designmentioning

confidence: 99%

Filtering Power Divider With Ultrawide Stopband and Wideband Low Radiation Loss Using Substrate Integrated Defected Ground Structure

Han

Tang

Deng

et al. 2021

IEEE Microw. Wireless Compon. Lett.

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Adaptive Synthesis for Resonator-Coupled Filters Based on Particle Swarm Optimization

Cited by 30 publications

References 24 publications

Design specification management with automated decision-making for reliable optimization of miniaturized microwave components

Design specification management with automated decision-making for reliable optimization of miniaturized microwave components

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Filtering Power Divider With Ultrawide Stopband and Wideband Low Radiation Loss Using Substrate Integrated Defected Ground Structure

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