An ARQ-Based Cooperative Relaying Scheme for 5G IoT Slice
Bibliographic record
Abstract
We introduce a new twist to the classical automatic repeat request (ARQ)-based cooperative relaying scheme. Here, we target a 5G slice for offering internet-of-things (IoT)-inspired services. In this 5G application segment (e.g., utility or smart metering), utility sensors or devices are expected to last for decades and, operate on tight power budgets, and the system can tolerate periodic stops to check the fidelity of previous transmissions. For this scenario, we employ a service provider-deployed relay that alleviates the IoT devices of their communication burdens. The source transmits for a certain time window or period without requiring acknowledgments (ACK) from the destination. At the end of this window, the destination sends an ACK. If negative, the relay is expected to assist. Thus, the relay transmits only intermittently, in either one transmission time slot or a few time slots, using a suitably chosen higherorder modulation constellation. If no error occurs, the source continues its transmission. The numerical results show that with just a few antennas at the relay, the new scheme provides both a reduction in the number of ARQ transmissions and a superior probability of bit error compared with a reference scheme. We adopt the two-parameter Weibull fading model to evaluate the performance of the proposed scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".