Low-complexity variable forgetting factor constant modulus RLS-based algorithm for blind adaptive beamforming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we propose a novel low-complexity variable forgetting factor (VFF) mechanism to enhance the performance of recursive least squares (RLS) algorithms for adaptive blind beamforming. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed algorithm operates in the generalized sidelobe canceler (GSC) structure for implementation. The proposed variable forgetting factor mechanism employs a new component updated by the time average of the constant modulus (CM) cost function, to adjust the forgetting factor. A complexity comparison is provided to show its advantages over existing methods. The study of the steady-state analysis is carried out. Simulation results which are presented for a nonstationary scenario illustrate that the proposed variable forgetting factor mechanism achieves a superior performance compared to existing algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it