End-to-End Batch Transmission in a Multihop and Multirate Wireless Network: Latency, Reliability, and Throughput Analysis
Bibliographic record
Abstract
This paper presents a novel Markov-based model for analyzing the end-to-end transmission of a batch of packets in a multihop wireless network using multirate transmission. The end-to-end reliability of this transmission (in terms of the number of packets delivered to the destination node) is controlled through different types of automatic repeat request (ARQ)-based error control mechanisms implemented at each node. For a batch of packets, we derive complete statistics (i.e., probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. Typical numerical results obtained from the model are validated by means of simulation. These results reveal the trade-off between end-to-end latency and end-to-end reliability, which would be an important issue in designing and engineering multihop wireless networks. Also, we demonstrate the usefulness of the proposed analytical model in predicting the latency and the reliability performances of TCP (transmission control protocol) in a multihop wireless scenario
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".