AFDA: Asynchronous Flipped Diversity ALOHA for Emerging Wireless Networks With Long and Heterogeneous Delay
Bibliographic record
Abstract
The design of random media access control (MAC) protocol renews great attention for emerging challenged wireless environments, where the propagation delay is long, heterogeneous, and/or varying, such as satellite or underwater acoustic sensor networks. In these environments, the existing MAC solutions based on slotted transmissions, carrier sensing, or channel reservation by control packets are no longer favorable or even feasible. In this paper, we propose the asynchronous flipped diversity ALOHA (AFDA) to tackle the challenges based on a new diversity transmission scheme. Different from the existing diversity transmission schemes, each data packet and its flipped replica are transmitted back to back, and the zigzag decoding technique is adopted to resolve collisions. The performance of AFDA has been evaluated by analysis and simulations. The results show that, without time synchronization or handshaking requirements, the performance of AFDA is unaffected by the duration or variation of the propagation delay, and it substantially improves system performance in terms of throughput, packet loss ratio, and network admission region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".