Performance Modeling and Analysis of a Class of ARQ Protocols in Multi-Hop Wireless Networks
Bibliographic record
Abstract
This paper models and analyzes the performances of a class of ARQ (automatic repeat request) protocols in a multi-hop wireless data network. The performance metric here is the number of transmissions required for successful delivery of a packet over a multi-hop path. By using a discrete-time Markov model, the distribution for the total required number of transmissions is modeled as phase type distribution. The effects of different network parameters-such as packet error rate in each hop, maximum number of allowable retransmissions at each hop and retransmission probability at each hop-on the required total number of transmissions are investigated. The novelty of this model is that the probability mass function (pmf) for the number of transmissions required for successful end-to-end delivery of a packet can be easily obtained under different hop-level error control policies. Using the pmf, the tradeoff between transmission energy and percentage of data delivery (i.e., reliability) in a multi-hop path can be analyzed. The analytical model is validated by simulations. While the proposed analytical framework is general enough to capture the impact of any MAC (medium access control) mechanism at each hop, we specifically present typical performance results under IEEE 802.11 DCF (distributed coordination function) MAC
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".