Queuing Performance of Multichannel S-ALOHA Systems With Correlated Arrivals
Bibliographic record
Abstract
In this paper, we examine the queuing performance of terminals in a multichannel centralized S-ALOHA system with a finite terminal population and finite queue size in each terminal, employing a uniform backoff (UB) algorithm with retry limit for collision resolution. The performance evaluations focus on uplink traffic from web browsing, which is modeled as a Markov-modulated Bernoulli process with correlated arrivals. We analyze the system performance in terms of system throughput, mean queue length, mean delay, the probability that a packet is dropped by retry limit, the probability that a packet is blocked by a full queue, and the mean and variance of packet retransmission time, in relation to the source correlation, number of channels, window size, and retry limit. For comparison, we also consider bufferless terminals with correlated arrivals. In addition, we evaluate by simulations the performance of terminals with finite queue size when they have perfect knowledge of the backlog size. Results from our study allow the parameters of the UB algorithm to properly be chosen to meet the access-level quality-of-service requirements.
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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 | 0.007 |
| 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".