Adaptive–Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks
Bibliographic record
Abstract
Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive–persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes’ data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".