Convergence properties of blind algorithms for base station CDMA receivers
Bibliographic record
Abstract
In this paper, the blind estimation of wireless CDMA receiver coefficients from the second order statistics of the signals is considered. Although many algorithms have been proposed so far, their performance analysis has always been carried out assuming perfect receiver coefficients estimation and/or under time-invariant conditions. In this article, we present some decision-directed blind algorithms and use a time-varying vector channel simulator to compare their performance with those of many previously proposed algorithms. It is shown that decision-directed chip-level algorithms can operate without the use of training sequences to avoid catastrophic error propagation, and that one should not expect an increase in performance from using least squares instead of least-mean-square. Furthermore the unpracticability of the bit-level algorithm (Gerlach 1992) and of the least significant algorithm (Liu and Zoltowski 1997) under a time varying environment is outlined. The performance of the principal component (Stanford) algorithm (Naguib and Paulraj 1995) is also studied.
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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 | 0.000 | 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 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".