Sub-6 GHz MIMO antenna design for 5G smartphones: A deep learning approach

Ahmed, Haroon; Zeng, Xiaoping; Bello, Hilal; Wang, Yang; Iqbal, Nayyar

doi:10.1016/j.aeue.2023.154716

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…In recent times the researchers have also made several approaches to propose innovative designs consisting of six [9], eight [10][11][12][13], ten [14] and twelve [15,16] element MIMO array antennas to improve the parameter of MIMO for mobile phone for sub-6 GHz (5G) communications. In [9], the authors have proposed a six-element MIMO array that operates below 2.5 GHz.…”

Section: Introductionmentioning

confidence: 99%

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Design and Performance Evaluation of a 6×6 MIMO Array with Partial Ground Plane for Sub-6 GHz Mobile Applications

2024

IJIES

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This article presents a 6×6 MIMO array optimized for sub-6 GHz mobile and wireless applications. Utilizing a partial ground plane (PGP), the array incorporates a single-element half-circle-slotted monopole antenna to achieve a wideband response. The compact design, measuring 75 × 150 mm² and only 0.508 mm thick, is engineered to fit 6.6-inch display smartphones. Operating between 2.82 and 5.95 GHz, the array offers a -10 dB bandwidth and a VSWR of less than 2. It also delivers port-to-port isolation less than -20 dB and a peak realized gain of 5.3 dBi, with an antenna efficiency of 71%. Comprehensive far-field radiation patterns are observed across all 6 ports. The array maintains an envelope correlation coefficient (ECC) under 0.004 and a diversity gain (DG) above 9.98, demonstrating robust performance. Specific absorption rate (SAR) results at 3.5 GHz confirms compliance with MIMO standards, with values as low as 0.13 W/kg. This high-performance MIMO array is a strong contender for sub-6 GHz 5G applications.

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Section: Introductionmentioning

confidence: 99%

“…In [12], an 8-element MIMO array design for 5G smartphones uses deep learning (DL) for efficient antenna dimension estimation, bypassing traditional electromagnetic simulation. Nevertheless, the chosen -6 dB bandwidth might lead to significant circuit-side reflection.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Evaluation of a 6×6 MIMO Array with Partial Ground Plane for Sub-6 GHz Mobile Applications

2024

IJIES

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“…However, in these referred designs, the number of samples are very large in each of the collected dataset which increases the computational overhead. Some of the recent studies related to design of MIMO antenna structures for sub 6 GHz 5 G NR n77/n78 frequency band applications are discussed in [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Deep learning assisted fractal slotted substrate MIMO antenna with characteristic mode analysis (CMA) for Sub-6 GHz n78 5 G NR applications: design, optimization and experimental validation

Babu,

Sudipta,

Sree

et al. 2023

Phys. Scr.

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This article presents the design and optimization of a Deep Learning assisted MIMO antenna for 5G New Radio (NR) applications in Sub-6GHz band. The Prototype of the simulated geometry has been practically modelled and experimentally tested to validate the theoretical results. The optimized two-port MIMO antenna of 48 × 24 mm2 size is designed using fractal slotted substrate geometry. The concept of characteristic mode analysis (CMA) method is performed to characterize the performance of the proposed slotted substrate monopole MIMO Antenna. Usually, Optimization techniques based on traditional electromagnetic simulation tools (CST, HFSS, etc) are time-consuming for the design purpose of complex MIMO antenna geometries. The simulation studies using electromagnetic simulation tools are needed to be performed repeatedly to evaluate the optimum geometrical parameters for achieving better acceptable performance. In order to solve this issue, Deep Learning (DL) method-based design optimization approach is considered as an effective technique for determining the optimum physical parameters. In this article, Deep learning method based Dual-Channel type Deep Neural Network (DC-DNN) optimization technique is suggested to estimate the S-parameters. The generated data is fed to DC-DNN acting as a surrogate model to map the input features with the reflection and transmission coefficients, thereby enabling the search for the optimum design parameters to obtain the best performance. The diversity parameters are observed within acceptable limits and the characteristic parameters related to impedance and radiation performance are best suitable for lower sub-6 GHz n78 (3300–3800 MHz) 5G NR network applications.

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Multifunctional Metasurface Polarizers Synthesis Using Effective Data Generation with Adaptive Attention and Space‐To‐Depth Enhanced Network

Ahmed,

Zeng,

Wang

et al. 2024

Advanced Optical Materials

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Conventional approaches for designing metasurfaces with the desired scattering response are often a time‐consuming and onerous process that heavily relies on the design experience and empirical methods. In this paper, a time and resource‐efficient design method is proposed for the synthesis of multiple metasurface polarizers with an efficient automated data generation process by utilizing the structure validation surrogate model (SVSM). Moreover, a novel attention and space‐to‐depth enhanced adaptive tandem network (ASE‐ATN) is proposed which integrates symmetry‐aware space‐to‐depth (SA‐SPD) along with the attention mechanism for efficient feature extraction. Several design examples of metasurfaces with linear polarization to orthogonal linear polarization (LP‐OLP) and/or linear polarization to circular polarization (LP‐CP) conversion capabilities are provided to verify the performance of the proposed method. The feasibility of the proposed method is demonstrated through the fabrication and measurement of a dual‐band dual‐polarization converter.

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Sub-6 GHz MIMO antenna design for 5G smartphones: A deep learning approach

Cited by 11 publications

References 9 publications

Design and Performance Evaluation of a 6×6 MIMO Array with Partial Ground Plane for Sub-6 GHz Mobile Applications

Design and Performance Evaluation of a 6×6 MIMO Array with Partial Ground Plane for Sub-6 GHz Mobile Applications

Deep learning assisted fractal slotted substrate MIMO antenna with characteristic mode analysis (CMA) for Sub-6 GHz n78 5 G NR applications: design, optimization and experimental validation

Multifunctional Metasurface Polarizers Synthesis Using Effective Data Generation with Adaptive Attention and Space‐To‐Depth Enhanced Network

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