Bibliographic record
Abstract
We propose a low complexity CDMA multiuser delay tracking receiver as an alternative to the more complex extended Kalman filter (EKF) based CDMA multiuser delay trackers. It integrates delay-tracking into CDMA multiuser detection by the delay-robust successive interference cancellation (SIC) technique, which was initially proposed for robust CDMA multiuser detection when there exists time delay estimation errors. When the delay error is small, the true user signature vector can be approximately expanded around the estimated time delay as a linear combination of an estimated signature vector and an error vector. This error vector is equivalent to a single-branch realization of the delay-late delay locked loop in a conventional single user CDMA receiver. The error signal is recursively estimated and cancelled in the multistage SIC iterations by using the tentative feedback decisions. Since the relative amplitude of the error signal provides information about the delay error, we propose the application of error signal feedback to improve the delay estimation. In a sliding window implementation, the delay-robust SIC is used to track multiple time varying user delays. For constant or slowly varying channels, a soft-decision function can be used to improve the delay-robust SIC. For fast fading channels, a linear decision function is used. Delay tracking results for both rectangular chip pulse and band-limited chip pulses are shown.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".