Fast Beamforming for Mobile Satellite Receiver Phased Arrays: Theory and Experiment
Bibliographic record
Abstract
The purpose of this paper is to present a robust and fast beamforming algorithm for the low-cost mobile phased array antennas. The proposed beamforming algorithm uses a sequentially perturbation gradient estimation method to update the control voltages of the phase shifters, with the objective of maximizing the received power by the array. This algorithm does not require either the knowledge of phase shifter characteristics or signal direction-of-arrival. Moreover, in this paper, the algorithm parameters are derived for the stationary and mobile platform configurations. For the stationary array, it is shown how the proper selection of the beamforming parameters limits the noise effects and increases the array output power. For the mobile array, a condition for the fast convergence is derived and the advantage of using nonuniform step size to update the control voltages is illustrated. When phase shifters suffer from the imbalanced insertion loss the proposed beamforming technique perturbs the phase-conjugate condition to increase the total received power. This algorithm has been implemented with the low-cost microwave components and applied to a Ku-band phased array antenna with 34 sub-arrays. The experimental results verify the broadband performance, and the fast convergence of the algorithm for different platform maneuvers.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".