Error recovery service for IEEE 802.11b protocols via adaptive forward error correction and dynamic packet sizing
Bibliographic record
Abstract
We develop a service that allows the current IEEE 802.11b MAC protocol to perform dynamic packet sizing and forward error correction. Our service, called ERSMAC, is designed to allow the deployment of the IEEE 802.11b protocol in industrial environments characterized by high BER and fast time variation. ERSMAC uses a maximum likelihood estimate of the BER to solve for the optimal packet size that maximizes the success probability of transmissions while minimizing the overhead cost. ERSMAC also implements an adaptive forward error correction scheme using Reed-Solomon codes such that every retransmission attempt has a higher probability of success than the previous attempt due to its association with a stronger RS code. Finally, we show, through simulations, that ERSMAC outperforms the original unmodified IEEE 802.116 protocol in terms of average throughput, average delay and efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".