Analysis of an Exponential Backoff Algorithm for Multipacket Reception Slotted ALOHA Systems
Bibliographic record
Abstract
This paper examines throughput and delay performances of multipacket reception (MPR) slotted ALOHA systems with the exponential backoff (EB) algorithm which consists of an initial transmission probability, exponentially decaying factor and a maximum number of backoff stages. We assume a finite population model and the saturated traffic condition where every terminal always has a packet to transmit. To show the general impacts of the EB algorithm's parameters on the system performance, we consider two MPR channels. In the first channel, all the packets transmitted cannot be successfully received, if the number of packets simultaneously transmitted exceeds a predefined threshold. In the second one, some of packets concurrently transmitted can be probabilistically received (captured). In numerical studies, we show how to adjust the parameters of EB algorithm given the MPR channel in order to achieve close-to-maximal system throughput, and discuss fair channel use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".