Penalty function based 2D adaptive digital beamforming for satellite communications
Bibliographic record
Abstract
Two dimentional (2D) adaptive digital beamforming is one of the promising smart antenna technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. However, the conventional 2D adaptive digital beamforming algorithms have the jitter and high sidelobe problems especially when the number of antenna elements is large. In this paper, a penalty function based 2D adaptive digital beamforming algorithm is proposed to estimate the weights of 2D antenna array. A penalty function is added to the original minimum square error (MSE) function. The formulas of estimating the weights are derived using the modified MSE function. System capacity and signal-to interference ratio (SIR) of the new 2D adaptive digital beamforming algorithm for satellite communications are also analyzed. Experimental results show that the penalty function based 2D adaptive beamforming algorithm can overcome the jitter and high sidelobe problems of the conventional adaptive algorithms. Compared with the fixed beam approach, the adaptive beamforming method has higher system capacity and SIR with the tradeoff of higher computation load.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".