A Dual-Band Reconfigurable Antenna Optimization Using Machine Learning Techniques
Bibliographic record
Abstract
This study presents an innovative methodology for the optimization of a reconfigurable antenna capable of dynamically adapting to four distinct radiation states: activation at the lower frequency band, activation at the higher frequency band, simultaneous activation at both bands, and a deactivated state. To achieve this adaptability, the antenna design incorporates two PIN diodes, facilitating seamless reconfiguration across multiple operational modes. A comprehensive dataset comprising of 2400 samples was generated to develop a surrogate model that accurately predicts the antenna's performance metrics. Utilizing this surrogate model, the Deep Deterministic Policy Gradient (DDPG) algorithm was applied to refine the antenna's structural parameters, ensuring optimal performance across all operational states. The proposed framework capitalizes on the surrogate model to expedite performance evaluations, substantially minimizing the computational burden typically associated with full-wave electromagnetic simulations. Results confirm the efficacy of the DDPG-driven optimization, demonstrating significant performance improvements across the designated frequency bands of 1.9 GHz and 2.4 GHz. This research highlights the transformative potential of reinforcement learning in the design and refinement of reconfigurable antennas, showcasing its applicability to complex, multi-objective engineering challenges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".