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Summary

This study introduces a new optimization algorithm for designing Internet of Things (IoT) antennas, significantly reducing computational costs for complex antenna designs. The method efficiently tunes antenna parameters for improved performance while meeting size constraints.

Keywords:
antenna designinternet of thingssurrogate-based optimizationtrust-region frameworkvariable-fidelity EM simulations

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Area of Science:

  • Electrical Engineering
  • Electromagnetics
  • Antenna Theory

Background:

  • Designing antennas for Internet of Things (IoT) applications involves balancing electrical performance, radiation characteristics, and strict physical size limitations.
  • Complex antenna structures require numerous geometry parameters for tuning, posing a challenge for conventional optimization methods.
  • High-fidelity electromagnetic (EM) analysis, while accurate, makes traditional optimization computationally prohibitive.

Purpose of the Study:

  • To develop a computationally efficient surrogate-assisted optimization algorithm for antenna design.
  • To minimize the antenna reflection coefficient within a specific bandwidth for improved input characteristics.
  • To reduce the computational cost associated with gradient estimation in antenna design optimization.

Main Methods:

  • Implementation of a novel surrogate-assisted optimization algorithm.
  • Utilization of variable-fidelity electromagnetic (EM) simulations.
  • Development of a gradient estimation procedure that monitors response sensitivities and suppresses updates for stable variables.

Main Results:

  • The proposed algorithm demonstrated significant computational efficiency compared to conventional methods.
  • Validation on three compact wideband antennas confirmed the algorithm's effectiveness.
  • The method outperformed trust region, pattern search, and existing surrogate-based optimization procedures.

Conclusions:

  • The novel surrogate-assisted optimization algorithm offers a computationally efficient solution for complex antenna design.
  • This approach enables the optimization of antenna input characteristics, such as reflection coefficient, within practical time and resource constraints.
  • The validated methodology provides a robust tool for designing high-performance antennas for IoT and other applications with stringent requirements.