Adaptive packetization for error-prone transmission over 802.11 WLANs with hidden terminals
Bibliographic record
Abstract
Collision and fading are the two main sources of packet loss in wireless local area networks (WLANs) and as such, both are affected by the packetization at the medium access control (MAC) layer.While a larger packet is preferred to balance protocol header overhead, a shorter packet is less vulnerable to packet loss due to channel fading errors or staggered collisions in the presence of hidden terminals. Direct collisions due to backoff are not affected by packet size. Recently, Krishnan et. al. have developed a new technique for estimating probabilities of various components of packet loss, namely, direct and staggered collisions and fading. Motivated by this work, in this paper, we exploit ways in which packetization can be used to improve throughput performance of WLANs. We first show analytically that the effective throughput is a unimodal function of the packet size when considering both channel fading and staggered collisions. We then develop a measurement-based algorithm based on golden section search to arrive at an optimal packet size for MAC-layer transmissions. Our simulations demonstrate that packetization based on our search algorithm can greatly improve the effective throughput of sensing-limited nodes, and reduce video frame transfer delay in WLANs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".