Globalized parametric optimization of microwave components by means of response features and inverse metamodels

Pietrenko‐Dabrowska, Anna; Kozieł, Sławomir

doi:10.1038/s41598-021-03095-0

Cited by 16 publications

(5 citation statements)

References 73 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 61 , 62 ], artificial neural networks (ANNs) were used for optimization of the antenna structures. Rigorous optimization methods were utilized to design both microwave components [ 63 , 64 , 65 , 66 ] and antenna structures [ 7 , 67 , 68 , 69 , 70 , 71 ]. In [ 67 ], the swarm intelligence (SI) algorithms operations were applied in a synthesis of the linear antenna array.…”

Section: Introductionmentioning

confidence: 99%

Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates

Roshani

Koziel

Yahya

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07λ edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as −46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.

show abstract

Section: Introductionmentioning

confidence: 99%

Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates

Roshani

Koziel

Yahya

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another option is the exploration of the specific structure of the system response (e.g., the allocation of resonances, in-band ripples, etc.) using techniques such as response feature technology 38 , 39 , or cognition-driven design 40 . Notwithstanding, one of the most popular approaches in the recent years have become surrogate-assisted methods 20 , 41 , 42 .…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based expedited parameter tuning of microwave passives by means of design requirement management and variable-resolution EM simulations

Kozieł

Pietrenko‐Dabrowska

Raef

2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

The importance of numerical optimization techniques has been continually growing in the design of microwave components over the recent years. Although reasonable initial designs can be obtained using circuit theory tools, precise parameter tuning is still necessary to account for effects such as electromagnetic (EM) cross coupling or radiation losses. EM-driven design closure is most often realized using gradient-based procedures, which are generally reliable as long as the initial design is sufficiently close to the optimum one. Otherwise, the search process may end up in a local optimum that is of insufficient quality. Furthermore, simulation-based optimization incurs considerable computational expenses, which are often impractically high. This paper proposes a novel parameter tuning procedure, combining a recently reported design specification management scheme, and variable-resolution EM models. The former allows for iteration-based automated modification of the design goals to make them accessible in every step of the search process, thereby improving its immunity to poor starting points. The knowledge-based procedure for the adjustment of the simulation model fidelity is based on the convergence status of the algorithm and discrepancy between the current and the original performance specifications. Due to using lower-resolution EM simulations in early phase of the optimization run, considerable CPU savings can be achieved, which are up to 60 percent over the gradient-based search employing design specifications management and numerical derivatives. Meanwhile, as demonstrated using three microstrip circuits, the computational speedup is obtained without design quality degradation.

show abstract

“…Perhaps the most significant bottleneck is its high computational cost when executed at the level of EM simulation models, otherwise necessary to ensure reliability of the process. While the costs are often manageable in the case of local (e.g., gradient-based) tuning, global or multi-objective optimization, as well as statistical design, are considerably more demanding 33 , 34 . Consequently, there have been numerous techniques developed to improve computational efficiency of EM-driven optimization.…”

Section: Introductionmentioning

confidence: 99%

Globalized simulation-driven miniaturization of microwave circuits by means of dimensionality-reduced constrained surrogates

Kozieł

Pietrenko‐Dabrowska

Mahrokh

2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Small size has become a crucial prerequisite in the design of modern microwave components. Miniaturized devices are essential for a number of application areas, including wireless communications, 5G/6G technology, wearable devices, or the internet of things. Notwithstanding, size reduction generally degrades the electrical performance of microwave systems. Therefore, trade-off solutions have to be sought that represent acceptable compromises between the ability to meet the design targets and physical compactness. From an optimization perspective, this poses a constrained task, which is computationally expensive because a reliable evaluation of microwave components has to rely on full-wave electromagnetic analysis. Furthermore, due to its constrained nature, size reduction is a multimodal problem, i.e., the results are highly dependent on the initial design. Thus, utilization of global search algorithms is advisable in principle, yet, often undoable in practice because of the associated computational expenses, especially when using nature-inspired procedures. This paper introduces a novel technique for globalized miniaturization of microwave components. Our technique starts by identifying the feasible region boundary, and by constructing a dimensionality-reduced surrogate model therein. Global optimization of the metamodel is followed by EM-driven local tuning. Application of the domain-confined surrogate ensures low cost of the entire procedure, further reduced by the incorporation of variable-fidelity EM simulations. Our framework is validated using two microstrip couplers, and compared to nature-inspired optimization, as well as gradient-based size reduction. The results indicate superior miniaturization rates and low running cost, which make the presented algorithm a potential candidate for efficient simulation-based design of compact structures.

show abstract

Globalized parametric optimization of microwave components by means of response features and inverse metamodels

Cited by 16 publications

References 73 publications

Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates

Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates

Knowledge-based expedited parameter tuning of microwave passives by means of design requirement management and variable-resolution EM simulations

Globalized simulation-driven miniaturization of microwave circuits by means of dimensionality-reduced constrained surrogates

Contact Info

Product

Resources

About