Bibliographic record
Abstract
An algorithm has been implemented in a CDMA cellular radio system to enable a 5 fold reduction in the stability requirement of the base station time reference oscillator. The algorithm adaptively models the frequency drift characteristics of the base station time reference OCXO whilst locked to a satellite time reference signal. If the satellite time reference is lost, the OCXO model is used to provide time correction of the base station reference oscillator for a holdover period of up to 24 hours during which repair or reacquisition of the satellite time reference signal is conducted. The novel algorithm uses two parallel Kalman filters to model adaptively the temperature and aging dependent frequency stability of the OCXO. The algorithm extracts the stability dependencies of the OCXO with respect to the noisy satellite time reference. Adaptive training of the Kalman filters occurs until satellite visibility is lost, and is re-initiated after the satellite time reference has been reacquired; thus, the algorithm is cognizant of changes in the OCXO frequency stability characteristics over its lifetime. In holdover, the Kalman filters operate as predictive state machines which generate a correction signal for the base station OCXO time reference based on the trained coefficients of the adaptive models. The correction algorithm has been trialed in a CDMA base station network and demonstrated to maintain the 10 MHz timing module reference oscillator to within 1.5 /spl mu/s of the CDMA system time over a holdover period of 24 hr, well within the 3GPP2 CDMA standard cumulative time error specification of 10 /spl mu/s over an 8 hr holdover period. Simulations indicate the feasibility of the algorithm to compensate for a further 10 fold reduction in reference oscillator stability whilst still meeting the 8 hr holdover specification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.002 |
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".