Multi-objective hybrid split-ring resonator and electromagnetic bandgap structure-based fractal antennas using hybrid metaheuristic framework for wireless applications
Bibliographic record
Abstract
Abstract Design closure and parameter optimisation are crucial in creating cutting-edge antennas. Antenna performance can be improved by fine-tuning preliminary designs created using theoretical considerations and rough dimension adjustment via supervised parameter sweeps. This paper introduces a frequency reconfigurable antenna design that can operate at 28/38 GHz frequencies to meet FCC and Ofcom standards for 5G applications and in the 18 GHz frequency band for K-band radar applications. A PIN diode is used in this design to configure multiple frequency bands. The antenna has a modified rectangular patch-like structure and two optimised plugins on either side. The study that is being presented focuses on maximising the parameters that are subject to optimisation, including length (Ls), width (Ws), strip line width (W1), and height (ht), where the antenna characteristic parameters such as directivity is tuned by a hybrid optimisation scheme called Elephant Clan Updated Grey Wolf Algorithm (ECU-GWA). Here, the performance of gain and directivity are optimally attained by considering parameters such as length, width, ground plane length, width, height, and feed offsets X and Y. The bandwidth of the proposed antenna at − 10 dB is 0.8 GHz, 1.94 GHz, and 7.92 GHz, respectively, at frequencies 18.5 GHz, 28.1 GHz, and 38.1 GHz. Also, according to the simulation results, in the 18 GHz, 28 GHz, and 38 GHz frequencies S11, the return loss is − 60.81 dB, − 56.31 dB, and − 14.19 dB, respectively. The proposed frequency reconfigurable antenna simulation results achieve gains of 4.41 dBi, 6.33 dBi, and 7.70 dBi at 18.5 GHz, 28.1 GHz, and 38.1 GHz, respectively. Also, a microstrip quarter-wave monopole antenna with an ellipsoidal-shaped complementary split-ring resonator-electromagnetic bandgap structure (ECSRR-EBG) structure has been designed based on a genetic algorithm having resonating at 2.9 GHz, 4.7 GHz, 6 GHz for WLAN applications. The gain of the suggested ECSRR metamaterial and EBG periodic structure, with and without the ECCSRR bow-tie antenna. This is done both in the lab and with numbers. The measured result shows that the ECSRR metamaterial boosts gain by 5.2 dBi at 5.9 GHz. At 5.57 GHz, the two-element MIMO antenna achieves its lowest ECC of 0.00081.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".