Bibliographic record
Abstract
This paper studies throughput improvement for TCP traffic in IEEE 802.11-based multihop ad hoc wireless networks. Due to the incompatibility between TCP and the IEEE 802.11 distributed coordination function (DCF) protocols, the reaction of TCP in case of packet losses can significantly reduce TCP end-to-end throughput. In this paper, we propose an opportunistic link scheduling (OLS), which is a simple enhancement to the IEEE 802.11 DCF protocol and intends to improve the compatibility between TCP and MAC layer protocols in multihop ad hoc networks. With OLS, a link with a good channel condition is allowed to transmit multiple packets consecutively as a burst, while the burst size depends on both physical channel fading and MAC layer collisions. The protocol also includes a mechanism to prevent starvation of nodes with poor channel conditions. An analytical model is developed for a four-hop chain to study the effect of the burst size and TCP congestion window size on the end-to-end transmission throughput in opportunistic link scheduling. Our results show that OLS can significantly improve the end-to-end transmission throughput, while keeping reasonably low transmission delay. The protocol is easy to implement and requires only slight modifications to the IEEE 802.11 protocol.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".