Adaptive blind widely linear CCM reduced-rank beamforming for large-scale antenna arrays
Bibliographic record
Abstract
In this paper, we propose an adaptive blind reduced-rank beamforming algorithm based on Krylov-subspace (KS) techniques and widely linear (WL) processing for non-circular signals. In contrast to the conventional WL processing approach, the properties of the augmented covariance matrix are exploited to derive a new structured WL beamforming scheme based on the generalized sidelobe canceler (GSC) structure. We develop a recursive least square (RLS) algorithm according to the constrained constant modulus (CCM) criterion to update the reduced-rank beamformer so obtained. A detailed signal-to-interference-plus noise ratio (SINR) analysis and a computational complexity analysis are carried out. Simulation results show that the proposed algorithm outperforms its linear counterpart and the full-rank algorithms, achieving the best convergence performance among all the analyzed methods with a relatively low complexity.1
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".