Irregular repetition slotted ALOHA with multiuser detection
Bibliographic record
Abstract
Irregular repetition slotted ALOHA is a random access scheme where users transmit multiple copies of a packet to the receiver to provide time-domain diversity; then, the receiver attempts to iteratively decode packets and cancel their interference contribution in other time slots, increasing the chance of resolving collisions and decoding more packets. This scheme was shown to support larger system loads compared to conventional slotted ALOHA. In the original scheme, collision resolution between l colliding packets is possible only when l - 1 of those packets are decoded in prior iterations, which is probable because of the other copies of each packet transmitted in other slots. However, although asymptotic analysis promises high efficiency of this scheme, the analysis relies on the large number of slots participating in the iterative collision resolution, which requires large receiver complexity and introduces delay. In this paper, we study this scheme for receivers capable of decoding multiple colliding packets jointly, which increases the chance of decoding more packets under large system loads. Asymptotic analysis for this generalized model is provided and it is shown by simulations that a multiuser detector supports larger loads for low number of slots jointly processed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".