Design and Optimization of MIMO Dielectric Resonator Antenna Using Machine Learning for Sub-6 GHz based on 5G IoT Applications

Ranjan, Pinku; Krishnan, Athulya; Dwivedi, Ajay Kumar; Singh, Suyash Kumar; Sharma, Anand

doi:10.1007/s13369-023-07830-9

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…In step 4, the proposed MIMO antenna's s-parameters are predicted using the best ML technique that was acquired in step 3. [15][16][17][18][19][20][21][22][23] The Mean Square Error (MSE), R 2 square (Accuracy) is given in Table 6 and Table 7, respectively. The predicted and actual values of KNN in various modes are shown in Figure 19.…”

Section: Methodology Of ML In Dual Port Mimomentioning

confidence: 99%

“…Applying the ML algorithms requires an enormous amount of data. [15][16][17][18][19][20][21][22][23]26,27 The dataset for the proposed work is created by varying the feeding structure's radius, and feed width as well as the CDRA's height, and radius in various configurations. A total of 413,876 datasets are generated in different configurations.…”

Section: Datasetmentioning

confidence: 99%

“…In this article, 5 ML algorithms, such as Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), and Decision Tree (DT), are used. [15][16][17][18][19][20][21][22][23] The comparison of dual port MIMO with tunable MIMO DRA is given in Table 1. The proposed antenna has the following attractive characteristics: i.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Optimization of Dual Port Dielectric Resonator Based Frequency Tunable MIMO Antenna with Machine Learning Approach for 5G New Radio Application

Rai,

Ranjan,

Chowdhury

et al. 2024

Int J Communication

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SummaryIn this article, a dual port Multiple Input Multiple Output (MIMO) cylindrical Dielectric Resonator (DR)‐based frequency tunable antenna with a machine learning (ML) approach for a 5G New Radio (NR) application is presented. According to the author's best knowledge, it is the first time‐frequency tunable MIMO hybrid DR with ML is reported. A dual port MIMO DRA is placed in the orthogonal configuration with the connected ground to obtain higher isolation in the entire frequency range. The proposed dual port antenna provides a total spectrum (TS) and tuning range (TR) of 98.99% and 80.93%, respectively. The different MIMO parameters, Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC), and Diversity Gain (DG) are investigated and found within the acceptable limits. The optimization of the proposed dual port tunable antenna is done through the various ML algorithms, including Artificial Neural Network (ANN), K‐Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB). The KNN ML algorithm provides more than 98% accuracy for predicting the S‐parameters in all configurations. Hence, the proposed antenna is suitable for 5G NR applications.

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Section: Methodology Of ML In Dual Port Mimomentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Optimization of Dual Port Dielectric Resonator Based Frequency Tunable MIMO Antenna with Machine Learning Approach for 5G New Radio Application

Rai,

Ranjan,

Chowdhury

et al. 2024

Int J Communication

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“…In open literature, lots of research article are available on MIMO DRAs. In these articles, authors are widely focused to improve the isolation level with the help of meta surface [7][8][9][10] or by using polarization diversity [11,12]. All these articles employed the Scattering parameter founded technique from reference [13], for the calculation of the Envelope Correlation Coefficient (ECC).…”

Section: Introductionmentioning

confidence: 99%

Spatially uncorrelated two port MIMO DRA using phase gradient PRS for dual directional pattern diversity

Yadav,

Verma,

Das

et al. 2024

Phys. Scr.

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This study presents a system utilizing a cylindrical shape ceramic along with a suspended frequency selective surface (FSS), designed for multiple input multiple output (MIMO) applications with dual ports. The proposed antenna system showcases dual pattern diversity properties introducing spatially uncorrelated pattern. Key features of the structured radiator include the arrangement of cylindrical ceramic and the incorporation of an FSS. The alumina ceramic are purposefully placed on the upper part of the dielectric with a joint ground plane. These ceramics are stimulated using circular aperture. To reduce the far-field correlation, FSS with similar phase gradients are placed at the top of DRA at a certain distance. This arrangement yields two distinct radiation patterns from the two ports, ensuring a diverse radiation pattern for the antenna. The proposed technique yields significant improvements, surpassing 10dB in isolation and achieving a 70% improvement in correlation values. The measured results validate the applicability of this antenna structure for WLAN access point applications within the 5.15-5.35 GHz frequency range.

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Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach

Kumar Rai,

Anuragi,

Mishra

et al. 2024

AEU - International Journal of Electronics and Communications

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Design and Optimization of MIMO Dielectric Resonator Antenna Using Machine Learning for Sub-6 GHz based on 5G IoT Applications

Cited by 6 publications

References 18 publications

Design and Optimization of Dual Port Dielectric Resonator Based Frequency Tunable MIMO Antenna with Machine Learning Approach for 5G New Radio Application

Design and Optimization of Dual Port Dielectric Resonator Based Frequency Tunable MIMO Antenna with Machine Learning Approach for 5G New Radio Application

Spatially uncorrelated two port MIMO DRA using phase gradient PRS for dual directional pattern diversity

Dual-band miniaturized composite right left handed transmission line ZOR antenna for microwave communication with machine learning approach

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