PCS networks with correlated arrival process and retrial phenomenon
Bibliographic record
Abstract
In this paper, the arrival of new calls and handoff calls to a personal communications services (PCS) network is modeled by a Markov arrival process (MAP) in which we allow correlation of the interarrival times among new calls, among handoff calls, as well as between these two kinds of calls. A new call will retry again if the first attempt is blocked. The PCS network consists of homogeneous cells and each cell consists of a finite number of channels. Under the general conditions that all random variables involved have general phase type (PH) distribution, we develop an explicit expression of the infinitesimal generator matrix of the Markov chain governing the network and find its complexity. This hits been a difficult matrix to obtain, judging from the works in the literature. It is very complex to develop and has not been previously obtained by other researchers. Some methods to find the stationary probability of the network are discussed. Particularly, we introduce an effective method, from which we can obtain the new call blocking probability and the handoff call failure probability. Also, the busy period of the orbit is introduced. This is an interesting measure from the viewpoint of network provider; its distribution and expectation are then obtained. The results presented in this paper can be used to provide some guidelines to performance evaluation for PCS network design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".