Optimization for next generation laptops enhancing performance and compactness
Bibliographic record
Abstract
This paper presents a computational intelligence-optimized multiband MIMO antenna for next-generation laptops, addressing the demand for compact, high-performance, and multiband wireless connectivity. The proposed 4-port MIMO configuration, with an overall footprint of 94.1 × 28.29 × 0.8 mm 3 , is optimized for seamless integration along the laptop’s top edge, where minimal width is crucial. It supports operation at 2.45 GHz, 5 GHz, and 6 GHz, enabling compatibility with dual and tri-band Wi-Fi 6E routers. To accelerate the design process and reduce manual iteration, machine learning (ML) algorithms including AdaBoost, SVM, CatBoost, and Decision Trees were employed. A simulation dataset was generated in CST studio by systematically varying critical antenna parameters. This dataset was used to train the ML models, enabling them to learn the nonlinear relationships between geometry and performance metrics such as S-parameters, gain, efficiency, envelope correlation coefficient (ECC), diversity gain. Upon training, the models predicted optimal design parameters for desired performance goals. The resulting antenna exhibited isolation greater than 16 dB, ECC below 0.08, and a measured realized gains of 0.73, 2.1, and 3 dBi across the operating bands. In addition, the channel capacity loss remained under 0.35 bits/s/Hz, confirming strong MIMO performance. This work highlights that incorporating computational intelligence into antenna design not only expedites the development process but also improves system efficiency, providing a scalable and intelligent solution for next-generation multifunctional, high-speed laptop platforms.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".