Miniaturised active integrated antennas: a co‐design approach
Bibliographic record
Abstract
A co‐design methodology for antenna miniaturisation with an integrated radio frequency (RF) amplifier is proposed for power‐efficient and compact RF front‐ends. This approach relaxes the standalone antenna matching requirement which is of interest in miniaturised antenna design. The detailed design and measurement procedure is presented and the concept is demonstrated with two examples for both narrowband and wideband scenarios considering transmitting and receiving front‐ends. The amplifiers and the antennas are co‐designed so that optimum performance is achieved in terms of gain, noise figure and efficiency. The antenna has the size of 0.033λ 0 2 at 2.45 GHz. It is miniaturised from its natural resonance frequency of 3.5–2.45 GHz which provides 51% of size reduction. The minimum bandwidth of the integrated antenna is found to be 37 and 150 MHz, respectively, for the narrowband and wideband designs. The active integrated antenna (AIA) efficiency is more than 47% when operating in the band of 2.45–2.6 GHz. The gain and noise figure of the AIA systems are optimised along with the antenna performance and found to be >10 dB and <2 dB, respectively. Measurement results are presented which are in well agreement with the design procedure and simulations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".