Machine learning-based dual-band circular MIMO antennas for high-performance 6 G IoT system
Bibliographic record
Abstract
• A novel 1 × 2 dual-band circular MIMO antenna is proposed for 6G THz wireless communication (3–8 THz). • Achieves dual-band operation at 5 THz (2.08 THz BW) and 7.67 THz (0.9 THz BW) with excellent return losses of –41.88 dB and –62.79 dB. • Provides superior isolation of –32.2 dB, outperforming existing THz MIMO designs. • Integrates six ML regression models (Random Forest, Extra Tree, XGBoost, KNN, Gaussian Process, CatBoost) for performance prediction. • Extra Tree Regression achieves the best accuracy (>87% for bandwidth prediction and precise isolation prediction). • Compact antenna size (80 × 160 μm²) makes it suitable for practical 6G THz integration. The demand for ultra-high data rates, ultra-low latency, and intelligent networking in 6G massive-scale IoT environments necessitates efficient design strategies for multiple-input multiple-output (MIMO) antenna systems. This study presents a novel, compact dual-band 2 × 2 MIMO antenna optimized through machine learning (ML) techniques, addressing bandwidth limitations and design complexities while reducing computational overhead compared to conventional simulation methods for 6G IoT applications. The proposed design features two orthogonally positioned T-slotted circular patch elements on a polyimide substrate, operating in the fundamental TM₁₀ mode to minimize electromagnetic leakage while enhancing radiation efficiency and directivity. Unlike traditional rectangular configurations, the slotted circular geometry delivers superior MIMO performance with broader bandwidth, enhanced isolation, reduced mutual coupling, and stable radiation characteristics in a compact footprint. Electromagnetic simulations were conducted using Computer Simulation Technology (CST) Studio Suite and cross-verified with Ansys HFSS, ensuring comprehensive validation. The antenna demonstrates exceptional dual-band performance with isolation of -32.2 dB, maximum bandwidth of 2.08 THz at 5 THz, and radiation efficiency of 87.43%. Diversity metrics include an Envelope Correlation Coefficient (ECC) of 0.009 and Diversity Gain (DG) of 9.96, confirming excellent MIMO characteristics. Six ML regression models were employed for predictive performance optimization, with the Extra Tree regression model achieving superior accuracy exceeding 87% for bandwidth prediction across all three frequency bands for both bandwidth and isolation parameters. The proposed circular MIMO antenna, validated through electromagnetic simulation and ML-based predictions, emerges as a promising candidate for terahertz 6G IoT applications with excellent impedance matching, radiation performance, and diversity characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".