Analysis of a clock‐recovery technique for circuit emulation services over packet networks
Bibliographic record
Abstract
Abstract One important requirement of circuit emulation services (CES) over packet networks is clock synchronization and timing distribution among the nodes. CES depends on reliable and high‐quality timing for operations. In the time division multiplexing (TDM) world, whether plesiochronous digital hierarchy (PDH), synchronous digital hierarchy (SDH) or synchronous optical network (SONET) based, timing and synchronization is inherent in the design of the network. However, when timing critical services such PDH and SDH/SONET are carried over packet network (e.g. IP, Ethernet, etc.), the timing element is lost and has to be carried across the packet network by other means. A well‐known and widely implemented technique for clock recovery in CES is one that is based on packet inter‐arrival time (sometimes called time difference of arrival) averaging. The technique is very simple to implement but provides good performance only when packet losses and packet delay variation (PDV) are very low and well controlled. This technique has been extensively analysed through simulations but has not been fully characterized analytically with correlated traffic in the literature. In this paper, we provide a full analytical examination of this well‐known clock recovery technique. We analyse the effects of correlation of the delay variation in the traffic stream on the quality of the clock recovered by a receiver. We prove analytically that, for a general input process, high correlation of the delay variation produces a large variance of the recovered clock. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".