Transformative Applications of AI in Antenna Design and Performance Optimization
Bibliographic record
Abstract
Antenna design and performance optimization have been slowly introduced to artificial intelligence. It addresses some of the most ancient issues in wireless communication systems and telecommunications. The three AI technologies that have transformed the traditional process of designing antenna designs, which was characterized by tedious and slow processes, are machine learning, deep learning, and generative models. The new technologies ease the designing process, controlling the significant features of efficiency, bandwidth, and gain, and designing portable, flexible, and switchable antenna systems. This paper discusses AI effects on antenna engineering in the current arena, such as 5G, future technologies, IoT devices, satellite communications and smart wearable devices. Also, it has concerns on complexity, data requirements as well as integrating with current processes. Moreover, it gives certain suggestions on the improvement and development. Through this review, the value of responsible research is highlighted and the application of AI should be made to enhance the antenna design. The increased order of the various domains, together with AI, will provide further opportunities in the future. The findings should be used as an aid in continued development and a stepping rocket towards future and better communication technologies with the help of AI- based antennas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".