Dual-Band SIW Antenna for mm-Wave IoT Applications with ML-Based Frequency Resonance Prediction
Bibliographic record
Abstract
For fifth-generation (5G) wireless communication applications, an innovative design featuring a substrate-integrated waveguide (SIW) cavity, coupled with a machine learning (ML) optimized elliptical slot antenna, has been introduced. The configuration operates efficiently in the millimeter-wave (mmWave) spectrum, supporting dual-band functionality to meet the high-performance requirements of next-generation communication systems. The antenna features compact dimensions of 25×30×0.13mm3relative to the dual-band operation. It achieves an impressive maximum directivity of 10.8 dBi and a peak efficiency of 91%, while, without an elliptical slot, the peak efficiency is 86%, making it highly effective for advanced wireless communication applications. The resonance frequency, fr, of the antenna is predicted using various supervised regression ML techniques. Multiple regression models are assessed using a range of metrics, including the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and accuracy (R2). Various regression models, including Random Forest Regressor (RFR), K-Neighbors Regressor (K-NR), Extreme Gradient Boosting (XGBoost), and Decision Tree Regressor (DTR), achieve an accuracy of approximately 95%. Lastly, the SIW antenna is well-suited for the Internet of Things (IoT), which uses 5G, according to CST and HFSS modeling findings and projected ML results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".