Trust region framework-based design of sub-6 GHz m-MIMO antenna and evaluation of SAR

• Ahmet Turgut ^[1] ; Begum Korunur Engiz ^[1]
  1. [1] Department of Electrical and Electronics Engineering, Ondokuz Mayis University, Samsun, Turkey
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 43, Nº Extra 3, 2024, págs. 669-690
• Idioma: inglés
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• Resumen

  • Purpose Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes.

    Design/methodology/approach This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF).

    Findings Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis.

    Originality/value The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.