Blind (training-like) decoder assisted beamforming for DS-CDMA systems
Bibliographic record
Abstract
We propose an iterative blind beamforming strategy for short-burst high-rate DS-CDMA systems. The blind strategy works by creating a set of "training sequences" in the receiver that is used as input to a semi-blind beamforming algorithm, thus producing a corresponding set of beamformers. The objective then becomes to find which beamformer gives the best performance (smallest bit error). Two challenges we face are: (1) to find a semi-blind algorithm that requires very few training symbols (to minimize the search time); (2) to find an appropriate criterion for picking the beamformer that offers the best performance. Different semi-blind algorithms and criteria are tested. The recently proposed SBCMACI (semi-blind CMA with channel identification) (Casella, I.R.S. et al., PIMRC, p.1972-6, 2002) is demonstrated to be ideal because of how few training symbols it needs for convergence. Of the tested criteria, one based on feedback from the decoder (essentially using trellis information) is shown to achieve nearly optimal performance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".