Adaptive microwave beamforming using fast perturbation
Bibliographic record
Abstract
MICROWAVE beamforming (MBF) is advantageous over digital beamforming (DBF) in several regards [1]. First, due to a lower number of RF down-converters and analog to digital converters (ADC), MBF structures have lower size, weight and battery power consumption. Second, due to weighting and combining of signals in the analog domain and avoiding quantization errors, MBFs have higher dynamic and suppression range. However, a problem with any MBF is the lack of antenna elements' signals in the processor which prohibits using existing DBF algorithms directly. Different perturbation techniques have been proposed to carry out adaptive beamforming using MBF structures [2]-[6]. In [7], [8] adaptive algorithms have been presented for MBF structures with phase shifters alone. In [9]-[12] perturbation techniques have been presented for microwave aerial beamforming. The main limitation in all these techniques is the long perturbation time.
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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 | 0.000 |
| 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".