End-to-End Queueing Performance Evaluation for Multiuser Wireless Relay Networks
Bibliographic record
Abstract
An analytical framework for the link-level end-to-end (e2e) queueing performance evaluation in a multiuser wireless relay network with automatic repeat request (ARQ)-based error control is presented. The e2e system consisting of a base station, a relay station, and multiple users is modeled as a probabilistic tandem of two finite queues for the relay and each user. The transmissions from the users are scheduled in a time-division multiplexing (TDM) fashion, i.e., in each time-slot only one user is in tandem with the relay's buffer with a certain probability. To make the analysis of such system tractable, the finite buffer of the relay is decomposed into smaller non-overlapping portions, each corresponding to an individual user's packets (i.e., per-user queueing). Using the decomposed model, radio link-level performance measures such as e2e packet loss rate, e2e delay and throughput are obtained analytically and compared with simulation results. As an application of this model, a method of obtaining optimum values for selection probabilities to maximize e2e aggregate throughput subject to users' individual delay constraints is presented.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".